Upload folder using huggingface_hub

.gitattributes

@@ -53,3 +53,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+blender/robot.blend filter=lfs diff=lfs merge=lfs -text
+blender/static_decorations.blend filter=lfs diff=lfs merge=lfs -text
+textures/sky.exr filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,25 @@
+---
+library_name: godot-rl
+tags:
+- deep-reinforcement-learning
+- reinforcement-learning
+- godot-rl
+- environments
+- video-games
+---
+
+A RL environment called RobotVolleyball for the Godot Game Engine.
+
+This environment was created with: https://github.com/edbeeching/godot_rl_agents 
+
+
+## Downloading the environment 
+
+After installing Godot RL Agents, download the environment with: 
+
+```
+gdrl.env_from_hub -r edbeeching/godot_rl_RobotVolleyball
+```
+
+
+

RobotVolleyball.csproj

@@ -0,0 +1,11 @@
+<Project Sdk="Godot.NET.Sdk/4.2.1">
+  <PropertyGroup>
+    <TargetFramework>net6.0</TargetFramework>
+    <TargetFramework Condition=" '$(GodotTargetPlatform)' == 'android' ">net7.0</TargetFramework>
+    <TargetFramework Condition=" '$(GodotTargetPlatform)' == 'ios' ">net8.0</TargetFramework>
+    <EnableDynamicLoading>true</EnableDynamicLoading>
+  </PropertyGroup>
+  <ItemGroup>
+    <PackageReference Include="Microsoft.ML.OnnxRuntime" Version="1.16.3" />
+  </ItemGroup>
+</Project>

RobotVolleyball.sln

@@ -0,0 +1,19 @@
+Microsoft Visual Studio Solution File, Format Version 12.00
+# Visual Studio 2012
+Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "RobotVolleyball", "RobotVolleyball.csproj", "{D16F0B71-2C5E-479E-B683-CBFF6C9F71E7}"
+EndProject
+Global
+	GlobalSection(SolutionConfigurationPlatforms) = preSolution
+	Debug|Any CPU = Debug|Any CPU
+	ExportDebug|Any CPU = ExportDebug|Any CPU
+	ExportRelease|Any CPU = ExportRelease|Any CPU
+	EndGlobalSection
+	GlobalSection(ProjectConfigurationPlatforms) = postSolution
+		{D16F0B71-2C5E-479E-B683-CBFF6C9F71E7}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
+		{D16F0B71-2C5E-479E-B683-CBFF6C9F71E7}.Debug|Any CPU.Build.0 = Debug|Any CPU
+		{D16F0B71-2C5E-479E-B683-CBFF6C9F71E7}.ExportDebug|Any CPU.ActiveCfg = ExportDebug|Any CPU
+		{D16F0B71-2C5E-479E-B683-CBFF6C9F71E7}.ExportDebug|Any CPU.Build.0 = ExportDebug|Any CPU
+		{D16F0B71-2C5E-479E-B683-CBFF6C9F71E7}.ExportRelease|Any CPU.ActiveCfg = ExportRelease|Any CPU
+		{D16F0B71-2C5E-479E-B683-CBFF6C9F71E7}.ExportRelease|Any CPU.Build.0 = ExportRelease|Any CPU
+	EndGlobalSection
+EndGlobal

addons/godot_rl_agents/controller/ai_controller_2d.gd

@@ -0,0 +1,113 @@
+extends Node2D
+class_name AIController2D
+
+enum ControlModes { INHERIT_FROM_SYNC, HUMAN, TRAINING, ONNX_INFERENCE, RECORD_EXPERT_DEMOS }
+@export var control_mode: ControlModes = ControlModes.INHERIT_FROM_SYNC
+@export var onnx_model_path := ""
+@export var reset_after := 1000
+
+@export_group("Record expert demos mode options")
+## Path where the demos will be saved. The file can later be used for imitation learning.
+@export var expert_demo_save_path: String
+## The action that erases the last recorded episode from the currently recorded data.
+@export var remove_last_episode_key: InputEvent
+## Action will be repeated for n frames. Will introduce control lag if larger than 1.
+## Can be used to ensure that action_repeat on inference and training matches
+## the recorded demonstrations.
+@export var action_repeat: int = 1
+
+var onnx_model: ONNXModel
+
+var heuristic := "human"
+var done := false
+var reward := 0.0
+var n_steps := 0
+var needs_reset := false
+
+var _player: Node2D
+
+
+func _ready():
+	add_to_group("AGENT")
+
+
+func init(player: Node2D):
+	_player = player
+
+
+#-- Methods that need implementing using the "extend script" option in Godot --#
+func get_obs() -> Dictionary:
+	assert(false, "the get_obs method is not implemented when extending from ai_controller")
+	return {"obs": []}
+
+
+func get_reward() -> float:
+	assert(false, "the get_reward method is not implemented when extending from ai_controller")
+	return 0.0
+
+
+func get_action_space() -> Dictionary:
+	assert(
+		false,
+		"the get get_action_space method is not implemented when extending from ai_controller"
+	)
+	return {
+		"example_actions_continous": {"size": 2, "action_type": "continuous"},
+		"example_actions_discrete": {"size": 2, "action_type": "discrete"},
+	}
+
+
+func set_action(action) -> void:
+	assert(false, "the get set_action method is not implemented when extending from ai_controller")
+
+
+#-----------------------------------------------------------------------------#
+
+
+#-- Methods that sometimes need implementing using the "extend script" option in Godot --#
+# Only needed if you are recording expert demos with this AIController
+func get_action() -> Array:
+	assert(false, "the get set_action method is not implemented in extended AIController but demo_recorder is used")
+	return []
+
+# -----------------------------------------------------------------------------#
+
+func _physics_process(delta):
+	n_steps += 1
+	if n_steps > reset_after:
+		needs_reset = true
+
+
+func get_obs_space():
+	# may need overriding if the obs space is complex
+	var obs = get_obs()
+	return {
+		"obs": {"size": [len(obs["obs"])], "space": "box"},
+	}
+
+
+func reset():
+	n_steps = 0
+	needs_reset = false
+
+
+func reset_if_done():
+	if done:
+		reset()
+
+
+func set_heuristic(h):
+	# sets the heuristic from "human" or "model" nothing to change here
+	heuristic = h
+
+
+func get_done():
+	return done
+
+
+func set_done_false():
+	done = false
+
+
+func zero_reward():
+	reward = 0.0

addons/godot_rl_agents/controller/ai_controller_3d.gd

@@ -0,0 +1,114 @@
+extends Node3D
+class_name AIController3D
+
+enum ControlModes { INHERIT_FROM_SYNC, HUMAN, TRAINING, ONNX_INFERENCE, RECORD_EXPERT_DEMOS }
+@export var control_mode: ControlModes = ControlModes.INHERIT_FROM_SYNC
+@export var onnx_model_path := ""
+@export var reset_after := 1000
+
+@export_group("Record expert demos mode options")
+## Path where the demos will be saved. The file can later be used for imitation learning.
+@export var expert_demo_save_path: String
+## The action that erases the last recorded episode from the currently recorded data.
+@export var remove_last_episode_key: InputEvent
+## Action will be repeated for n frames. Will introduce control lag if larger than 1.
+## Can be used to ensure that action_repeat on inference and training matches
+## the recorded demonstrations.
+@export var action_repeat: int = 1
+
+var onnx_model: ONNXModel
+
+var heuristic := "human"
+var done := false
+var reward := 0.0
+var n_steps := 0
+var needs_reset := false
+
+var _player: Node3D
+
+
+func _ready():
+	add_to_group("AGENT")
+
+
+func init(player: Node3D):
+	_player = player
+
+
+#-- Methods that need implementing using the "extend script" option in Godot --#
+func get_obs() -> Dictionary:
+	assert(false, "the get_obs method is not implemented when extending from ai_controller")
+	return {"obs": []}
+
+
+func get_reward() -> float:
+	assert(false, "the get_reward method is not implemented when extending from ai_controller")
+	return 0.0
+
+
+func get_action_space() -> Dictionary:
+	assert(
+		false,
+		"the get get_action_space method is not implemented when extending from ai_controller"
+	)
+	return {
+		"example_actions_continous": {"size": 2, "action_type": "continuous"},
+		"example_actions_discrete": {"size": 2, "action_type": "discrete"},
+	}
+
+
+func set_action(action) -> void:
+	assert(false, "the get set_action method is not implemented when extending from ai_controller")
+
+
+#-----------------------------------------------------------------------------#
+
+
+#-- Methods that sometimes need implementing using the "extend script" option in Godot --#
+# Only needed if you are recording expert demos with this AIController
+func get_action() -> Array:
+	assert(false, "the get set_action method is not implemented in extended AIController but demo_recorder is used")
+	return []
+
+# -----------------------------------------------------------------------------#
+
+
+func _physics_process(delta):
+	n_steps += 1
+	if n_steps > reset_after:
+		needs_reset = true
+
+
+func get_obs_space():
+	# may need overriding if the obs space is complex
+	var obs = get_obs()
+	return {
+		"obs": {"size": [len(obs["obs"])], "space": "box"},
+	}
+
+
+func reset():
+	n_steps = 0
+	needs_reset = false
+
+
+func reset_if_done():
+	if done:
+		reset()
+
+
+func set_heuristic(h):
+	# sets the heuristic from "human" or "model" nothing to change here
+	heuristic = h
+
+
+func get_done():
+	return done
+
+
+func set_done_false():
+	done = false
+
+
+func zero_reward():
+	reward = 0.0

addons/godot_rl_agents/godot_rl_agents.gd

@@ -0,0 +1,16 @@
+@tool
+extends EditorPlugin
+
+
+func _enter_tree():
+	# Initialization of the plugin goes here.
+	# Add the new type with a name, a parent type, a script and an icon.
+	add_custom_type("Sync", "Node", preload("sync.gd"), preload("icon.png"))
+	#add_custom_type("RaycastSensor2D2", "Node", preload("raycast_sensor_2d.gd"), preload("icon.png"))
+
+
+func _exit_tree():
+	# Clean-up of the plugin goes here.
+	# Always remember to remove it from the engine when deactivated.
+	remove_custom_type("Sync")
+	#remove_custom_type("RaycastSensor2D2")

addons/godot_rl_agents/icon.png.import

@@ -0,0 +1,34 @@
+[remap]
+
+importer="texture"
+type="CompressedTexture2D"
+uid="uid://g2osotvjho5v"
+path="res://.godot/imported/icon.png-45a871b53434e556222f5901d598ab34.ctex"
+metadata={
+"vram_texture": false
+}
+
+[deps]
+
+source_file="res://addons/godot_rl_agents/icon.png"
+dest_files=["res://.godot/imported/icon.png-45a871b53434e556222f5901d598ab34.ctex"]
+
+[params]
+
+compress/mode=0
+compress/high_quality=false
+compress/lossy_quality=0.7
+compress/hdr_compression=1
+compress/normal_map=0
+compress/channel_pack=0
+mipmaps/generate=false
+mipmaps/limit=-1
+roughness/mode=0
+roughness/src_normal=""
+process/fix_alpha_border=true
+process/premult_alpha=false
+process/normal_map_invert_y=false
+process/hdr_as_srgb=false
+process/hdr_clamp_exposure=false
+process/size_limit=0
+detect_3d/compress_to=1

addons/godot_rl_agents/onnx/csharp/ONNXInference.cs

@@ -0,0 +1,103 @@
+using Godot;
+using Microsoft.ML.OnnxRuntime;
+using Microsoft.ML.OnnxRuntime.Tensors;
+using System.Collections.Generic;
+using System.Linq;
+
+namespace GodotONNX
+{
+	/// <include file='docs/ONNXInference.xml' path='docs/members[@name="ONNXInference"]/ONNXInference/*'/>
+	public partial class ONNXInference : GodotObject
+	{
+
+		private InferenceSession session;
+		/// <summary>
+		/// Path to the ONNX model. Use Initialize to change it. 
+		/// </summary>
+		private string modelPath;
+		private int batchSize;
+
+		private SessionOptions SessionOpt;
+
+		//init function
+		/// <include file='docs/ONNXInference.xml' path='docs/members[@name="ONNXInference"]/Initialize/*'/>
+		public void Initialize(string Path, int BatchSize)
+		{
+			modelPath = Path;
+			batchSize = BatchSize;
+            SessionOpt = SessionConfigurator.MakeConfiguredSessionOptions();
+            session = LoadModel(modelPath);
+
+		}
+		/// <include file='docs/ONNXInference.xml' path='docs/members[@name="ONNXInference"]/Run/*'/>
+		public Godot.Collections.Dictionary<string, Godot.Collections.Array<float>> RunInference(Godot.Collections.Array<float> obs, int state_ins)
+		{
+			//Current model: Any (Godot Rl Agents)
+			//Expects a tensor of shape [batch_size, input_size] type float named obs and a tensor of shape [batch_size] type float named state_ins
+
+			//Fill the input tensors
+			// create span from inputSize
+			var span = new float[obs.Count]; //There's probably a better way to do this
+			for (int i = 0; i < obs.Count; i++)
+			{
+				span[i] = obs[i];
+			}
+
+			IReadOnlyCollection<NamedOnnxValue> inputs = new List<NamedOnnxValue>
+			{
+			NamedOnnxValue.CreateFromTensor("obs", new DenseTensor<float>(span, new int[] { batchSize, obs.Count })),
+			NamedOnnxValue.CreateFromTensor("state_ins", new DenseTensor<float>(new float[] { state_ins }, new int[] { batchSize }))
+			};
+			IReadOnlyCollection<string> outputNames = new List<string> { "output", "state_outs" }; //ONNX is sensible to these names, as well as the input names
+
+			IDisposableReadOnlyCollection<DisposableNamedOnnxValue> results; 
+			//We do not use "using" here so we get a better exception explaination later
+			try
+			{
+				results = session.Run(inputs, outputNames);
+			}
+			catch (OnnxRuntimeException e)
+			{
+				//This error usually means that the model is not compatible with the input, beacause of the input shape (size)
+				GD.Print("Error at inference: ", e);
+				return null;
+			}
+			//Can't convert IEnumerable<float> to Variant, so we have to convert it to an array or something
+			Godot.Collections.Dictionary<string, Godot.Collections.Array<float>> output = new Godot.Collections.Dictionary<string, Godot.Collections.Array<float>>();
+			DisposableNamedOnnxValue output1 = results.First();
+			DisposableNamedOnnxValue output2 = results.Last();
+			Godot.Collections.Array<float> output1Array = new Godot.Collections.Array<float>();
+			Godot.Collections.Array<float> output2Array = new Godot.Collections.Array<float>();
+
+			foreach (float f in output1.AsEnumerable<float>())
+			{
+				output1Array.Add(f);
+			}
+
+			foreach (float f in output2.AsEnumerable<float>())
+			{
+				output2Array.Add(f);
+			}
+
+			output.Add(output1.Name, output1Array);
+			output.Add(output2.Name, output2Array);
+
+			//Output is a dictionary of arrays, ex: { "output" : [0.1, 0.2, 0.3, 0.4, ...], "state_outs" : [0.5, ...]}
+			results.Dispose();
+			return output;
+		}
+		/// <include file='docs/ONNXInference.xml' path='docs/members[@name="ONNXInference"]/Load/*'/>
+		public InferenceSession LoadModel(string Path)
+		{
+			using Godot.FileAccess file = FileAccess.Open(Path, Godot.FileAccess.ModeFlags.Read);
+			byte[] model = file.GetBuffer((int)file.GetLength());
+			//file.Close(); file.Dispose(); //Close the file, then dispose the reference.
+			return new InferenceSession(model, SessionOpt); //Load the model
+		}
+		public void FreeDisposables()
+		{
+			session.Dispose();
+			SessionOpt.Dispose();
+		}
+	}
+}

addons/godot_rl_agents/onnx/csharp/SessionConfigurator.cs

@@ -0,0 +1,131 @@
+using Godot;
+using Microsoft.ML.OnnxRuntime;
+
+namespace GodotONNX
+{
+	/// <include file='docs/SessionConfigurator.xml' path='docs/members[@name="SessionConfigurator"]/SessionConfigurator/*'/>
+
+	public static class SessionConfigurator
+	{
+		public enum ComputeName
+		{
+			CUDA,
+			ROCm,
+			DirectML,
+			CoreML,
+			CPU
+		}
+
+        /// <include file='docs/SessionConfigurator.xml' path='docs/members[@name="SessionConfigurator"]/GetSessionOptions/*'/>
+        public static SessionOptions MakeConfiguredSessionOptions()
+        {
+            SessionOptions sessionOptions = new();
+            SetOptions(sessionOptions);
+            return sessionOptions;
+        }
+
+        private static void SetOptions(SessionOptions sessionOptions)
+        {
+            sessionOptions.LogSeverityLevel = OrtLoggingLevel.ORT_LOGGING_LEVEL_WARNING;
+            ApplySystemSpecificOptions(sessionOptions);
+        }
+
+        /// <include file='docs/SessionConfigurator.xml' path='docs/members[@name="SessionConfigurator"]/SystemCheck/*'/>
+        static public void ApplySystemSpecificOptions(SessionOptions sessionOptions)
+        {
+            //Most code for this function is verbose only, the only reason it exists is to track
+            //implementation progress of the different compute APIs.
+
+            //December 2022: CUDA is not working. 
+
+            string OSName = OS.GetName(); //Get OS Name
+
+            //ComputeName ComputeAPI = ComputeCheck(); //Get Compute API
+            //                                         //TODO: Get CPU architecture
+
+            //Linux can use OpenVINO (C#) on x64 and ROCm on x86 (GDNative/C++)
+            //Windows can use OpenVINO (C#) on x64
+            //TODO: try TensorRT instead of CUDA
+            //TODO: Use OpenVINO for Intel Graphics
+
+            // Temporarily using CPU on all platforms to avoid errors detected with DML
+            ComputeName ComputeAPI = ComputeName.CPU;
+
+            //match OS and Compute API
+            GD.Print($"OS: {OSName} Compute API: {ComputeAPI}");
+
+            // CPU is set by default without appending necessary
+            // sessionOptions.AppendExecutionProvider_CPU(0);
+
+            /*
+            switch (OSName)
+            {
+                case "Windows": //Can use CUDA, DirectML
+                    if (ComputeAPI is ComputeName.CUDA)
+                    {
+                        //CUDA 
+                        //sessionOptions.AppendExecutionProvider_CUDA(0);
+                        //sessionOptions.AppendExecutionProvider_DML(0);
+                    }
+                    else if (ComputeAPI is ComputeName.DirectML)
+                    {
+                        //DirectML
+                        //sessionOptions.AppendExecutionProvider_DML(0);
+                    }
+                    break;
+                case "X11": //Can use CUDA, ROCm
+                    if (ComputeAPI is ComputeName.CUDA)
+                    {
+                        //CUDA
+                        //sessionOptions.AppendExecutionProvider_CUDA(0);
+                    }
+                    if (ComputeAPI is ComputeName.ROCm)
+                    {
+                        //ROCm, only works on x86 
+                        //Research indicates that this has to be compiled as a GDNative plugin
+                        //GD.Print("ROCm not supported yet, using CPU.");
+                        //sessionOptions.AppendExecutionProvider_CPU(0);
+                    }
+                    break;
+                case "macOS": //Can use CoreML
+                    if (ComputeAPI is ComputeName.CoreML)
+                    { //CoreML
+                      //TODO: Needs testing
+                        //sessionOptions.AppendExecutionProvider_CoreML(0);
+                        //CoreML on ARM64, out of the box, on x64 needs .tar file from GitHub
+                    }
+                    break;
+                default:
+                    GD.Print("OS not Supported.");
+                    break;
+            }
+            */
+        }
+
+
+        /// <include file='docs/SessionConfigurator.xml' path='docs/members[@name="SessionConfigurator"]/ComputeCheck/*'/>
+        public static ComputeName ComputeCheck()
+		{
+			string adapterName = Godot.RenderingServer.GetVideoAdapterName();
+			//string adapterVendor = Godot.RenderingServer.GetVideoAdapterVendor();
+			adapterName = adapterName.ToUpper(new System.Globalization.CultureInfo(""));
+			//TODO: GPU vendors for MacOS, what do they even use these days?
+		  
+			if (adapterName.Contains("INTEL"))
+			{
+				return ComputeName.DirectML;
+			}
+			if (adapterName.Contains("AMD") || adapterName.Contains("RADEON"))
+			{
+				return ComputeName.DirectML;
+			}
+			if (adapterName.Contains("NVIDIA"))
+			{
+				return ComputeName.CUDA;
+			}
+
+			GD.Print("Graphics Card not recognized."); //Should use CPU
+			return ComputeName.CPU;
+		}
+	}
+}

addons/godot_rl_agents/onnx/csharp/docs/ONNXInference.xml

@@ -0,0 +1,31 @@
+<docs>
+	<members name="ONNXInference">
+		<ONNXInference>
+			<summary>
+       		The main <c>ONNXInference</c> Class that handles the inference process.
+        	</summary>
+		</ONNXInference>
+   		<Initialize>
+        	<summary>
+			Starts the inference process. 
+			</summary>
+			<param name="Path">Path to the ONNX model, expects a path inside resources.</param>
+			<param name="BatchSize">How many observations will the model recieve.</param>
+		</Initialize>
+		<Run>
+			<summary>
+			Runs the given input through the model and returns the output.
+			</summary>
+			<param name="obs">Dictionary containing all observations.</param>
+			<param name="state_ins">How many different agents are creating these observations.</param>
+			<returns>A Dictionary of arrays, containing instructions based on the observations.</returns>
+		</Run>
+		<Load>
+			<summary>
+			Loads the given model into the inference process, using the best Execution provider available.
+			</summary>
+			<param name="Path">Path to the ONNX model, expects a path inside resources.</param>
+			<returns>InferenceSession ready to run.</returns>
+		</Load>
+	</members>
+</docs>

addons/godot_rl_agents/onnx/csharp/docs/SessionConfigurator.xml

@@ -0,0 +1,29 @@
+<docs>
+	<members name="SessionConfigurator">
+		<SessionConfigurator>
+			<summary>
+       		The main <c>SessionConfigurator</c> Class that handles the execution options and providers for the inference process.
+        	</summary>
+		</SessionConfigurator>
+  		<GetSessionOptions>
+    	    <summary>
+    	    Creates a SessionOptions with all available execution providers.
+    	    </summary>
+			<returns>SessionOptions with all available execution providers.</returns>
+    	</GetSessionOptions>
+    	<SystemCheck>
+        	<summary>
+        	Appends any execution provider available in the current system.  
+        	</summary>
+			<remarks>
+				This function is mainly verbose for tracking implementation progress of different compute APIs. 
+			</remarks>
+    	</SystemCheck>
+    	<ComputeCheck>
+        	<summary>
+        		Checks for available GPUs.
+        	</summary>
+			<returns>An integer identifier for each compute platform.</returns>
+    	</ComputeCheck>
+	</members>
+</docs>

addons/godot_rl_agents/onnx/wrapper/ONNX_wrapper.gd

@@ -0,0 +1,27 @@
+extends Resource
+class_name ONNXModel
+var inferencer_script = load("res://addons/godot_rl_agents/onnx/csharp/ONNXInference.cs")
+
+var inferencer = null
+
+
+# Must provide the path to the model and the batch size
+func _init(model_path, batch_size):
+	inferencer = inferencer_script.new()
+	inferencer.Initialize(model_path, batch_size)
+
+
+# This function is the one that will be called from the game,
+# requires the observation as an array and the state_ins as an int
+# returns an Array containing the action the model takes.
+func run_inference(obs: Array, state_ins: int) -> Dictionary:
+	if inferencer == null:
+		printerr("Inferencer not initialized")
+		return {}
+	return inferencer.RunInference(obs, state_ins)
+
+
+func _notification(what):
+	if what == NOTIFICATION_PREDELETE:
+		inferencer.FreeDisposables()
+		inferencer.free()

addons/godot_rl_agents/plugin.cfg

@@ -0,0 +1,7 @@
+[plugin]
+
+name="GodotRLAgents"
+description="Custom nodes for the godot rl agents toolkit	"
+author="Edward Beeching"
+version="0.1"
+script="godot_rl_agents.gd"

addons/godot_rl_agents/sensors/sensors_2d/ExampleRaycastSensor2D.tscn

@@ -0,0 +1,48 @@
+[gd_scene load_steps=5 format=3 uid="uid://ddeq7mn1ealyc"]
+
+[ext_resource type="Script" path="res://addons/godot_rl_agents/sensors/sensors_2d/RaycastSensor2D.gd" id="1"]
+
+[sub_resource type="GDScript" id="2"]
+script/source = "extends Node2D
+
+
+
+func _physics_process(delta: float) -> void:
+    print(\"step start\")
+    
+"
+
+[sub_resource type="GDScript" id="1"]
+script/source = "extends RayCast2D
+
+var steps = 1
+
+func _physics_process(delta: float) -> void:
+    print(\"processing raycast\")
+    steps += 1
+    if steps % 2:
+        force_raycast_update()
+
+    print(is_colliding())
+"
+
+[sub_resource type="CircleShape2D" id="3"]
+
+[node name="ExampleRaycastSensor2D" type="Node2D"]
+script = SubResource("2")
+
+[node name="ExampleAgent" type="Node2D" parent="."]
+position = Vector2(573, 314)
+rotation = 0.286234
+
+[node name="RaycastSensor2D" type="Node2D" parent="ExampleAgent"]
+script = ExtResource("1")
+
+[node name="TestRayCast2D" type="RayCast2D" parent="."]
+script = SubResource("1")
+
+[node name="StaticBody2D" type="StaticBody2D" parent="."]
+position = Vector2(1, 52)
+
+[node name="CollisionShape2D" type="CollisionShape2D" parent="StaticBody2D"]
+shape = SubResource("3")

addons/godot_rl_agents/sensors/sensors_2d/GridSensor2D.gd

@@ -0,0 +1,235 @@
+@tool
+extends ISensor2D
+class_name GridSensor2D
+
+@export var debug_view := false:
+	get:
+		return debug_view
+	set(value):
+		debug_view = value
+		_update()
+
+@export_flags_2d_physics var detection_mask := 0:
+	get:
+		return detection_mask
+	set(value):
+		detection_mask = value
+		_update()
+
+@export var collide_with_areas := false:
+	get:
+		return collide_with_areas
+	set(value):
+		collide_with_areas = value
+		_update()
+
+@export var collide_with_bodies := true:
+	get:
+		return collide_with_bodies
+	set(value):
+		collide_with_bodies = value
+		_update()
+
+@export_range(1, 200, 0.1) var cell_width := 20.0:
+	get:
+		return cell_width
+	set(value):
+		cell_width = value
+		_update()
+
+@export_range(1, 200, 0.1) var cell_height := 20.0:
+	get:
+		return cell_height
+	set(value):
+		cell_height = value
+		_update()
+
+@export_range(1, 21, 2, "or_greater") var grid_size_x := 3:
+	get:
+		return grid_size_x
+	set(value):
+		grid_size_x = value
+		_update()
+
+@export_range(1, 21, 2, "or_greater") var grid_size_y := 3:
+	get:
+		return grid_size_y
+	set(value):
+		grid_size_y = value
+		_update()
+
+var _obs_buffer: PackedFloat64Array
+var _rectangle_shape: RectangleShape2D
+var _collision_mapping: Dictionary
+var _n_layers_per_cell: int
+
+var _highlighted_cell_color: Color
+var _standard_cell_color: Color
+
+
+func get_observation():
+	return _obs_buffer
+
+
+func _update():
+	if Engine.is_editor_hint():
+		if is_node_ready():
+			_spawn_nodes()
+
+
+func _ready() -> void:
+	_set_colors()
+
+	if Engine.is_editor_hint():
+		if get_child_count() == 0:
+			_spawn_nodes()
+	else:
+		_spawn_nodes()
+
+
+func _set_colors() -> void:
+	_standard_cell_color = Color(100.0 / 255.0, 100.0 / 255.0, 100.0 / 255.0, 100.0 / 255.0)
+	_highlighted_cell_color = Color(255.0 / 255.0, 100.0 / 255.0, 100.0 / 255.0, 100.0 / 255.0)
+
+
+func _get_collision_mapping() -> Dictionary:
+	# defines which layer is mapped to which cell obs index
+	var total_bits = 0
+	var collision_mapping = {}
+	for i in 32:
+		var bit_mask = 2 ** i
+		if (detection_mask & bit_mask) > 0:
+			collision_mapping[i] = total_bits
+			total_bits += 1
+
+	return collision_mapping
+
+
+func _spawn_nodes():
+	for cell in get_children():
+		cell.name = "_%s" % cell.name  # Otherwise naming below will fail
+		cell.queue_free()
+
+	_collision_mapping = _get_collision_mapping()
+	#prints("collision_mapping", _collision_mapping, len(_collision_mapping))
+	# allocate memory for the observations
+	_n_layers_per_cell = len(_collision_mapping)
+	_obs_buffer = PackedFloat64Array()
+	_obs_buffer.resize(grid_size_x * grid_size_y * _n_layers_per_cell)
+	_obs_buffer.fill(0)
+	#prints(len(_obs_buffer), _obs_buffer )
+
+	_rectangle_shape = RectangleShape2D.new()
+	_rectangle_shape.set_size(Vector2(cell_width, cell_height))
+
+	var shift := Vector2(
+		-(grid_size_x / 2) * cell_width,
+		-(grid_size_y / 2) * cell_height,
+	)
+
+	for i in grid_size_x:
+		for j in grid_size_y:
+			var cell_position = Vector2(i * cell_width, j * cell_height) + shift
+			_create_cell(i, j, cell_position)
+
+
+func _create_cell(i: int, j: int, position: Vector2):
+	var cell := Area2D.new()
+	cell.position = position
+	cell.name = "GridCell %s %s" % [i, j]
+	cell.modulate = _standard_cell_color
+
+	if collide_with_areas:
+		cell.area_entered.connect(_on_cell_area_entered.bind(i, j))
+		cell.area_exited.connect(_on_cell_area_exited.bind(i, j))
+
+	if collide_with_bodies:
+		cell.body_entered.connect(_on_cell_body_entered.bind(i, j))
+		cell.body_exited.connect(_on_cell_body_exited.bind(i, j))
+
+	cell.collision_layer = 0
+	cell.collision_mask = detection_mask
+	cell.monitorable = true
+	add_child(cell)
+	cell.set_owner(get_tree().edited_scene_root)
+
+	var col_shape := CollisionShape2D.new()
+	col_shape.shape = _rectangle_shape
+	col_shape.name = "CollisionShape2D"
+	cell.add_child(col_shape)
+	col_shape.set_owner(get_tree().edited_scene_root)
+
+	if debug_view:
+		var quad = MeshInstance2D.new()
+		quad.name = "MeshInstance2D"
+		var quad_mesh = QuadMesh.new()
+
+		quad_mesh.set_size(Vector2(cell_width, cell_height))
+
+		quad.mesh = quad_mesh
+		cell.add_child(quad)
+		quad.set_owner(get_tree().edited_scene_root)
+
+
+func _update_obs(cell_i: int, cell_j: int, collision_layer: int, entered: bool):
+	for key in _collision_mapping:
+		var bit_mask = 2 ** key
+		if (collision_layer & bit_mask) > 0:
+			var collison_map_index = _collision_mapping[key]
+
+			var obs_index = (
+				(cell_i * grid_size_x * _n_layers_per_cell)
+				+ (cell_j * _n_layers_per_cell)
+				+ collison_map_index
+			)
+			#prints(obs_index, cell_i, cell_j)
+			if entered:
+				_obs_buffer[obs_index] += 1
+			else:
+				_obs_buffer[obs_index] -= 1
+
+
+func _toggle_cell(cell_i: int, cell_j: int):
+	var cell = get_node_or_null("GridCell %s %s" % [cell_i, cell_j])
+
+	if cell == null:
+		print("cell not found, returning")
+
+	var n_hits = 0
+	var start_index = (cell_i * grid_size_x * _n_layers_per_cell) + (cell_j * _n_layers_per_cell)
+	for i in _n_layers_per_cell:
+		n_hits += _obs_buffer[start_index + i]
+
+	if n_hits > 0:
+		cell.modulate = _highlighted_cell_color
+	else:
+		cell.modulate = _standard_cell_color
+
+
+func _on_cell_area_entered(area: Area2D, cell_i: int, cell_j: int):
+	#prints("_on_cell_area_entered", cell_i, cell_j)
+	_update_obs(cell_i, cell_j, area.collision_layer, true)
+	if debug_view:
+		_toggle_cell(cell_i, cell_j)
+	#print(_obs_buffer)
+
+
+func _on_cell_area_exited(area: Area2D, cell_i: int, cell_j: int):
+	#prints("_on_cell_area_exited", cell_i, cell_j)
+	_update_obs(cell_i, cell_j, area.collision_layer, false)
+	if debug_view:
+		_toggle_cell(cell_i, cell_j)
+
+
+func _on_cell_body_entered(body: Node2D, cell_i: int, cell_j: int):
+	#prints("_on_cell_body_entered", cell_i, cell_j)
+	_update_obs(cell_i, cell_j, body.collision_layer, true)
+	if debug_view:
+		_toggle_cell(cell_i, cell_j)
+
+
+func _on_cell_body_exited(body: Node2D, cell_i: int, cell_j: int):
+	#prints("_on_cell_body_exited", cell_i, cell_j)
+	_update_obs(cell_i, cell_j, body.collision_layer, false)
+	if debug_view:
+		_toggle_cell(cell_i, cell_j)

addons/godot_rl_agents/sensors/sensors_2d/ISensor2D.gd

@@ -0,0 +1,25 @@
+extends Node2D
+class_name ISensor2D
+
+var _obs: Array = []
+var _active := false
+
+
+func get_observation():
+	pass
+
+
+func activate():
+	_active = true
+
+
+func deactivate():
+	_active = false
+
+
+func _update_observation():
+	pass
+
+
+func reset():
+	pass

addons/godot_rl_agents/sensors/sensors_2d/RaycastSensor2D.gd

@@ -0,0 +1,118 @@
+@tool
+extends ISensor2D
+class_name RaycastSensor2D
+
+@export_flags_2d_physics var collision_mask := 1:
+	get:
+		return collision_mask
+	set(value):
+		collision_mask = value
+		_update()
+
+@export var collide_with_areas := false:
+	get:
+		return collide_with_areas
+	set(value):
+		collide_with_areas = value
+		_update()
+
+@export var collide_with_bodies := true:
+	get:
+		return collide_with_bodies
+	set(value):
+		collide_with_bodies = value
+		_update()
+
+@export var n_rays := 16.0:
+	get:
+		return n_rays
+	set(value):
+		n_rays = value
+		_update()
+
+@export_range(5, 3000, 5.0) var ray_length := 200:
+	get:
+		return ray_length
+	set(value):
+		ray_length = value
+		_update()
+@export_range(5, 360, 5.0) var cone_width := 360.0:
+	get:
+		return cone_width
+	set(value):
+		cone_width = value
+		_update()
+
+@export var debug_draw := true:
+	get:
+		return debug_draw
+	set(value):
+		debug_draw = value
+		_update()
+
+var _angles = []
+var rays := []
+
+
+func _update():
+	if Engine.is_editor_hint():
+		if debug_draw:
+			_spawn_nodes()
+		else:
+			for ray in get_children():
+				if ray is RayCast2D:
+					remove_child(ray)
+
+
+func _ready() -> void:
+	_spawn_nodes()
+
+
+func _spawn_nodes():
+	for ray in rays:
+		ray.queue_free()
+	rays = []
+
+	_angles = []
+	var step = cone_width / (n_rays)
+	var start = step / 2 - cone_width / 2
+
+	for i in n_rays:
+		var angle = start + i * step
+		var ray = RayCast2D.new()
+		ray.set_target_position(
+			Vector2(ray_length * cos(deg_to_rad(angle)), ray_length * sin(deg_to_rad(angle)))
+		)
+		ray.set_name("node_" + str(i))
+		ray.enabled = false
+		ray.collide_with_areas = collide_with_areas
+		ray.collide_with_bodies = collide_with_bodies
+		ray.collision_mask = collision_mask
+		add_child(ray)
+		rays.append(ray)
+
+		_angles.append(start + i * step)
+
+
+func get_observation() -> Array:
+	return self.calculate_raycasts()
+
+
+func calculate_raycasts() -> Array:
+	var result = []
+	for ray in rays:
+		ray.enabled = true
+		ray.force_raycast_update()
+		var distance = _get_raycast_distance(ray)
+		result.append(distance)
+		ray.enabled = false
+	return result
+
+
+func _get_raycast_distance(ray: RayCast2D) -> float:
+	if !ray.is_colliding():
+		return 0.0
+
+	var distance = (global_position - ray.get_collision_point()).length()
+	distance = clamp(distance, 0.0, ray_length)
+	return (ray_length - distance) / ray_length

addons/godot_rl_agents/sensors/sensors_2d/RaycastSensor2D.tscn

@@ -0,0 +1,7 @@
+[gd_scene load_steps=2 format=3 uid="uid://drvfihk5esgmv"]
+
+[ext_resource type="Script" path="res://addons/godot_rl_agents/sensors/sensors_2d/RaycastSensor2D.gd" id="1"]
+
+[node name="RaycastSensor2D" type="Node2D"]
+script = ExtResource("1")
+n_rays = 17.0

addons/godot_rl_agents/sensors/sensors_3d/ExampleRaycastSensor3D.tscn

@@ -0,0 +1,6 @@
+[gd_scene format=3 uid="uid://biu787qh4woik"]
+
+[node name="ExampleRaycastSensor3D" type="Node3D"]
+
+[node name="Camera3D" type="Camera3D" parent="."]
+transform = Transform3D(1, 0, 0, 0, 1, 0, 0, 0, 1, 0.804183, 0, 2.70146)

addons/godot_rl_agents/sensors/sensors_3d/GridSensor3D.gd

@@ -0,0 +1,258 @@
+@tool
+extends ISensor3D
+class_name GridSensor3D
+
+@export var debug_view := false:
+	get:
+		return debug_view
+	set(value):
+		debug_view = value
+		_update()
+
+@export_flags_3d_physics var detection_mask := 0:
+	get:
+		return detection_mask
+	set(value):
+		detection_mask = value
+		_update()
+
+@export var collide_with_areas := false:
+	get:
+		return collide_with_areas
+	set(value):
+		collide_with_areas = value
+		_update()
+
+@export var collide_with_bodies := false:
+	# NOTE! The sensor will not detect StaticBody3D, add an area to static bodies to detect them
+	get:
+		return collide_with_bodies
+	set(value):
+		collide_with_bodies = value
+		_update()
+
+@export_range(0.1, 2, 0.1) var cell_width := 1.0:
+	get:
+		return cell_width
+	set(value):
+		cell_width = value
+		_update()
+
+@export_range(0.1, 2, 0.1) var cell_height := 1.0:
+	get:
+		return cell_height
+	set(value):
+		cell_height = value
+		_update()
+
+@export_range(1, 21, 2, "or_greater") var grid_size_x := 3:
+	get:
+		return grid_size_x
+	set(value):
+		grid_size_x = value
+		_update()
+
+@export_range(1, 21, 2, "or_greater") var grid_size_z := 3:
+	get:
+		return grid_size_z
+	set(value):
+		grid_size_z = value
+		_update()
+
+var _obs_buffer: PackedFloat64Array
+var _box_shape: BoxShape3D
+var _collision_mapping: Dictionary
+var _n_layers_per_cell: int
+
+var _highlighted_box_material: StandardMaterial3D
+var _standard_box_material: StandardMaterial3D
+
+
+func get_observation():
+	return _obs_buffer
+
+
+func reset():
+	_obs_buffer.fill(0)
+
+
+func _update():
+	if Engine.is_editor_hint():
+		if is_node_ready():
+			_spawn_nodes()
+
+
+func _ready() -> void:
+	_make_materials()
+
+	if Engine.is_editor_hint():
+		if get_child_count() == 0:
+			_spawn_nodes()
+	else:
+		_spawn_nodes()
+
+
+func _make_materials() -> void:
+	if _highlighted_box_material != null and _standard_box_material != null:
+		return
+
+	_standard_box_material = StandardMaterial3D.new()
+	_standard_box_material.set_transparency(1)  # ALPHA
+	_standard_box_material.albedo_color = Color(
+		100.0 / 255.0, 100.0 / 255.0, 100.0 / 255.0, 100.0 / 255.0
+	)
+
+	_highlighted_box_material = StandardMaterial3D.new()
+	_highlighted_box_material.set_transparency(1)  # ALPHA
+	_highlighted_box_material.albedo_color = Color(
+		255.0 / 255.0, 100.0 / 255.0, 100.0 / 255.0, 100.0 / 255.0
+	)
+
+
+func _get_collision_mapping() -> Dictionary:
+	# defines which layer is mapped to which cell obs index
+	var total_bits = 0
+	var collision_mapping = {}
+	for i in 32:
+		var bit_mask = 2 ** i
+		if (detection_mask & bit_mask) > 0:
+			collision_mapping[i] = total_bits
+			total_bits += 1
+
+	return collision_mapping
+
+
+func _spawn_nodes():
+	for cell in get_children():
+		cell.name = "_%s" % cell.name  # Otherwise naming below will fail
+		cell.queue_free()
+
+	_collision_mapping = _get_collision_mapping()
+	#prints("collision_mapping", _collision_mapping, len(_collision_mapping))
+	# allocate memory for the observations
+	_n_layers_per_cell = len(_collision_mapping)
+	_obs_buffer = PackedFloat64Array()
+	_obs_buffer.resize(grid_size_x * grid_size_z * _n_layers_per_cell)
+	_obs_buffer.fill(0)
+	#prints(len(_obs_buffer), _obs_buffer )
+
+	_box_shape = BoxShape3D.new()
+	_box_shape.set_size(Vector3(cell_width, cell_height, cell_width))
+
+	var shift := Vector3(
+		-(grid_size_x / 2) * cell_width,
+		0,
+		-(grid_size_z / 2) * cell_width,
+	)
+
+	for i in grid_size_x:
+		for j in grid_size_z:
+			var cell_position = Vector3(i * cell_width, 0.0, j * cell_width) + shift
+			_create_cell(i, j, cell_position)
+
+
+func _create_cell(i: int, j: int, position: Vector3):
+	var cell := Area3D.new()
+	cell.position = position
+	cell.name = "GridCell %s %s" % [i, j]
+
+	if collide_with_areas:
+		cell.area_entered.connect(_on_cell_area_entered.bind(i, j))
+		cell.area_exited.connect(_on_cell_area_exited.bind(i, j))
+
+	if collide_with_bodies:
+		cell.body_entered.connect(_on_cell_body_entered.bind(i, j))
+		cell.body_exited.connect(_on_cell_body_exited.bind(i, j))
+
+#		cell.body_shape_entered.connect(_on_cell_body_shape_entered.bind(i, j))
+#		cell.body_shape_exited.connect(_on_cell_body_shape_exited.bind(i, j))
+
+	cell.collision_layer = 0
+	cell.collision_mask = detection_mask
+	cell.monitorable = true
+	cell.input_ray_pickable = false
+	add_child(cell)
+	cell.set_owner(get_tree().edited_scene_root)
+
+	var col_shape := CollisionShape3D.new()
+	col_shape.shape = _box_shape
+	col_shape.name = "CollisionShape3D"
+	cell.add_child(col_shape)
+	col_shape.set_owner(get_tree().edited_scene_root)
+
+	if debug_view:
+		var box = MeshInstance3D.new()
+		box.name = "MeshInstance3D"
+		var box_mesh = BoxMesh.new()
+
+		box_mesh.set_size(Vector3(cell_width, cell_height, cell_width))
+		box_mesh.material = _standard_box_material
+
+		box.mesh = box_mesh
+		cell.add_child(box)
+		box.set_owner(get_tree().edited_scene_root)
+
+
+func _update_obs(cell_i: int, cell_j: int, collision_layer: int, entered: bool):
+	for key in _collision_mapping:
+		var bit_mask = 2 ** key
+		if (collision_layer & bit_mask) > 0:
+			var collison_map_index = _collision_mapping[key]
+
+			var obs_index = (
+				(cell_i * grid_size_x * _n_layers_per_cell)
+				+ (cell_j * _n_layers_per_cell)
+				+ collison_map_index
+			)
+			#prints(obs_index, cell_i, cell_j)
+			if entered:
+				_obs_buffer[obs_index] += 1
+			else:
+				_obs_buffer[obs_index] -= 1
+
+
+func _toggle_cell(cell_i: int, cell_j: int):
+	var cell = get_node_or_null("GridCell %s %s" % [cell_i, cell_j])
+
+	if cell == null:
+		print("cell not found, returning")
+
+	var n_hits = 0
+	var start_index = (cell_i * grid_size_x * _n_layers_per_cell) + (cell_j * _n_layers_per_cell)
+	for i in _n_layers_per_cell:
+		n_hits += _obs_buffer[start_index + i]
+
+	var cell_mesh = cell.get_node_or_null("MeshInstance3D")
+	if n_hits > 0:
+		cell_mesh.mesh.material = _highlighted_box_material
+	else:
+		cell_mesh.mesh.material = _standard_box_material
+
+
+func _on_cell_area_entered(area: Area3D, cell_i: int, cell_j: int):
+	#prints("_on_cell_area_entered", cell_i, cell_j)
+	_update_obs(cell_i, cell_j, area.collision_layer, true)
+	if debug_view:
+		_toggle_cell(cell_i, cell_j)
+	#print(_obs_buffer)
+
+
+func _on_cell_area_exited(area: Area3D, cell_i: int, cell_j: int):
+	#prints("_on_cell_area_exited", cell_i, cell_j)
+	_update_obs(cell_i, cell_j, area.collision_layer, false)
+	if debug_view:
+		_toggle_cell(cell_i, cell_j)
+
+
+func _on_cell_body_entered(body: Node3D, cell_i: int, cell_j: int):
+	#prints("_on_cell_body_entered", cell_i, cell_j)
+	_update_obs(cell_i, cell_j, body.collision_layer, true)
+	if debug_view:
+		_toggle_cell(cell_i, cell_j)
+
+
+func _on_cell_body_exited(body: Node3D, cell_i: int, cell_j: int):
+	#prints("_on_cell_body_exited", cell_i, cell_j)
+	_update_obs(cell_i, cell_j, body.collision_layer, false)
+	if debug_view:
+		_toggle_cell(cell_i, cell_j)

addons/godot_rl_agents/sensors/sensors_3d/ISensor3D.gd

@@ -0,0 +1,25 @@
+extends Node3D
+class_name ISensor3D
+
+var _obs: Array = []
+var _active := false
+
+
+func get_observation():
+	pass
+
+
+func activate():
+	_active = true
+
+
+func deactivate():
+	_active = false
+
+
+func _update_observation():
+	pass
+
+
+func reset():
+	pass

addons/godot_rl_agents/sensors/sensors_3d/RGBCameraSensor3D.gd

@@ -0,0 +1,21 @@
+extends Node3D
+class_name RGBCameraSensor3D
+var camera_pixels = null
+
+@onready var camera_texture := $Control/TextureRect/CameraTexture as Sprite2D
+@onready var sub_viewport := $SubViewport as SubViewport
+
+
+func get_camera_pixel_encoding():
+	return camera_texture.get_texture().get_image().get_data().hex_encode()
+
+
+func get_camera_shape() -> Array:
+	assert(
+		sub_viewport.size.x >= 36 and sub_viewport.size.y >= 36,
+		"SubViewport size must be 36x36 or larger."
+	)
+	if sub_viewport.transparent_bg:
+		return [4, sub_viewport.size.y, sub_viewport.size.x]
+	else:
+		return [3, sub_viewport.size.y, sub_viewport.size.x]

addons/godot_rl_agents/sensors/sensors_3d/RGBCameraSensor3D.tscn

@@ -0,0 +1,41 @@
+[gd_scene load_steps=3 format=3 uid="uid://baaywi3arsl2m"]
+
+[ext_resource type="Script" path="res://addons/godot_rl_agents/sensors/sensors_3d/RGBCameraSensor3D.gd" id="1"]
+
+[sub_resource type="ViewportTexture" id="1"]
+viewport_path = NodePath("SubViewport")
+
+[node name="RGBCameraSensor3D" type="Node3D"]
+script = ExtResource("1")
+
+[node name="RemoteTransform3D" type="RemoteTransform3D" parent="."]
+remote_path = NodePath("../SubViewport/Camera3D")
+
+[node name="SubViewport" type="SubViewport" parent="."]
+size = Vector2i(32, 32)
+render_target_update_mode = 3
+
+[node name="Camera3D" type="Camera3D" parent="SubViewport"]
+near = 0.5
+
+[node name="Control" type="Control" parent="."]
+layout_mode = 3
+anchors_preset = 15
+anchor_right = 1.0
+anchor_bottom = 1.0
+grow_horizontal = 2
+grow_vertical = 2
+
+[node name="TextureRect" type="ColorRect" parent="Control"]
+layout_mode = 0
+offset_left = 1096.0
+offset_top = 534.0
+offset_right = 1114.0
+offset_bottom = 552.0
+scale = Vector2(10, 10)
+color = Color(0.00784314, 0.00784314, 0.00784314, 1)
+
+[node name="CameraTexture" type="Sprite2D" parent="Control/TextureRect"]
+texture = SubResource("1")
+offset = Vector2(9, 9)
+flip_v = true

addons/godot_rl_agents/sensors/sensors_3d/RaycastSensor3D.gd

@@ -0,0 +1,185 @@
+@tool
+extends ISensor3D
+class_name RayCastSensor3D
+@export_flags_3d_physics var collision_mask = 1:
+	get:
+		return collision_mask
+	set(value):
+		collision_mask = value
+		_update()
+@export_flags_3d_physics var boolean_class_mask = 1:
+	get:
+		return boolean_class_mask
+	set(value):
+		boolean_class_mask = value
+		_update()
+
+@export var n_rays_width := 6.0:
+	get:
+		return n_rays_width
+	set(value):
+		n_rays_width = value
+		_update()
+
+@export var n_rays_height := 6.0:
+	get:
+		return n_rays_height
+	set(value):
+		n_rays_height = value
+		_update()
+
+@export var ray_length := 10.0:
+	get:
+		return ray_length
+	set(value):
+		ray_length = value
+		_update()
+
+@export var cone_width := 60.0:
+	get:
+		return cone_width
+	set(value):
+		cone_width = value
+		_update()
+
+@export var cone_height := 60.0:
+	get:
+		return cone_height
+	set(value):
+		cone_height = value
+		_update()
+
+@export var collide_with_areas := false:
+	get:
+		return collide_with_areas
+	set(value):
+		collide_with_areas = value
+		_update()
+
+@export var collide_with_bodies := true:
+	get:
+		return collide_with_bodies
+	set(value):
+		collide_with_bodies = value
+		_update()
+
+@export var class_sensor := false
+
+var rays := []
+var geo = null
+
+
+func _update():
+	if Engine.is_editor_hint():
+		if is_node_ready():
+			_spawn_nodes()
+
+
+func _ready() -> void:
+	if Engine.is_editor_hint():
+		if get_child_count() == 0:
+			_spawn_nodes()
+	else:
+		_spawn_nodes()
+
+
+func _spawn_nodes():
+	print("spawning nodes")
+	for ray in get_children():
+		ray.queue_free()
+	if geo:
+		geo.clear()
+	#$Lines.remove_points()
+	rays = []
+
+	var horizontal_step = cone_width / (n_rays_width)
+	var vertical_step = cone_height / (n_rays_height)
+
+	var horizontal_start = horizontal_step / 2 - cone_width / 2
+	var vertical_start = vertical_step / 2 - cone_height / 2
+
+	var points = []
+
+	for i in n_rays_width:
+		for j in n_rays_height:
+			var angle_w = horizontal_start + i * horizontal_step
+			var angle_h = vertical_start + j * vertical_step
+			#angle_h = 0.0
+			var ray = RayCast3D.new()
+			var cast_to = to_spherical_coords(ray_length, angle_w, angle_h)
+			ray.set_target_position(cast_to)
+
+			points.append(cast_to)
+
+			ray.set_name("node_" + str(i) + " " + str(j))
+			ray.enabled = true
+			ray.collide_with_bodies = collide_with_bodies
+			ray.collide_with_areas = collide_with_areas
+			ray.collision_mask = collision_mask
+			add_child(ray)
+			ray.set_owner(get_tree().edited_scene_root)
+			rays.append(ray)
+			ray.force_raycast_update()
+
+
+#    if Engine.editor_hint:
+#        _create_debug_lines(points)
+
+
+func _create_debug_lines(points):
+	if not geo:
+		geo = ImmediateMesh.new()
+		add_child(geo)
+
+	geo.clear()
+	geo.begin(Mesh.PRIMITIVE_LINES)
+	for point in points:
+		geo.set_color(Color.AQUA)
+		geo.add_vertex(Vector3.ZERO)
+		geo.add_vertex(point)
+	geo.end()
+
+
+func display():
+	if geo:
+		geo.display()
+
+
+func to_spherical_coords(r, inc, azimuth) -> Vector3:
+	return Vector3(
+		r * sin(deg_to_rad(inc)) * cos(deg_to_rad(azimuth)),
+		r * sin(deg_to_rad(azimuth)),
+		r * cos(deg_to_rad(inc)) * cos(deg_to_rad(azimuth))
+	)
+
+
+func get_observation() -> Array:
+	return self.calculate_raycasts()
+
+
+func calculate_raycasts() -> Array:
+	var result = []
+	for ray in rays:
+		ray.set_enabled(true)
+		ray.force_raycast_update()
+		var distance = _get_raycast_distance(ray)
+
+		result.append(distance)
+		if class_sensor:
+			var hit_class: float = 0
+			if ray.get_collider():
+				var hit_collision_layer = ray.get_collider().collision_layer
+				hit_collision_layer = hit_collision_layer & collision_mask
+				hit_class = (hit_collision_layer & boolean_class_mask) > 0
+			result.append(float(hit_class))
+		ray.set_enabled(false)
+	return result
+
+
+func _get_raycast_distance(ray: RayCast3D) -> float:
+	if !ray.is_colliding():
+		return 0.0
+
+	var distance = (global_transform.origin - ray.get_collision_point()).length()
+	distance = clamp(distance, 0.0, ray_length)
+	return (ray_length - distance) / ray_length

addons/godot_rl_agents/sensors/sensors_3d/RaycastSensor3D.tscn

@@ -0,0 +1,27 @@
+[gd_scene load_steps=2 format=3 uid="uid://b803cbh1fmy66"]
+
+[ext_resource type="Script" path="res://addons/godot_rl_agents/sensors/sensors_3d/RaycastSensor3D.gd" id="1"]
+
+[node name="RaycastSensor3D" type="Node3D"]
+script = ExtResource("1")
+n_rays_width = 4.0
+n_rays_height = 2.0
+ray_length = 11.0
+
+[node name="node_1 0" type="RayCast3D" parent="."]
+target_position = Vector3(-1.38686, -2.84701, 10.5343)
+
+[node name="node_1 1" type="RayCast3D" parent="."]
+target_position = Vector3(-1.38686, 2.84701, 10.5343)
+
+[node name="node_2 0" type="RayCast3D" parent="."]
+target_position = Vector3(1.38686, -2.84701, 10.5343)
+
+[node name="node_2 1" type="RayCast3D" parent="."]
+target_position = Vector3(1.38686, 2.84701, 10.5343)
+
+[node name="node_3 0" type="RayCast3D" parent="."]
+target_position = Vector3(4.06608, -2.84701, 9.81639)
+
+[node name="node_3 1" type="RayCast3D" parent="."]
+target_position = Vector3(4.06608, 2.84701, 9.81639)

addons/godot_rl_agents/sync.gd

@@ -0,0 +1,540 @@
+extends Node
+
+# --fixed-fps 2000 --disable-render-loop
+
+enum ControlModes { HUMAN, TRAINING, ONNX_INFERENCE }
+@export var control_mode: ControlModes = ControlModes.TRAINING
+@export_range(1, 10, 1, "or_greater") var action_repeat := 8
+@export_range(0, 10, 0.1, "or_greater") var speed_up := 1.0
+@export var onnx_model_path := ""
+
+# Onnx model stored for each requested path
+var onnx_models: Dictionary
+
+@onready var start_time = Time.get_ticks_msec()
+
+const MAJOR_VERSION := "0"
+const MINOR_VERSION := "7" 
+const DEFAULT_PORT := "11008"
+const DEFAULT_SEED := "1"
+var stream: StreamPeerTCP = null
+var connected = false
+var message_center
+var should_connect = true
+
+var all_agents: Array
+var agents_training: Array
+var agents_inference: Array
+var agents_heuristic: Array
+
+## For recording expert demos
+var agent_demo_record: Node
+## Stores recorded trajectories
+var demo_trajectories: Array
+## A trajectory includes obs: Array, acts: Array, terminal (set in Python env instead)
+var current_demo_trajectory: Array
+
+var need_to_send_obs = false
+var args = null
+var initialized = false
+var just_reset = false
+var onnx_model = null
+var n_action_steps = 0
+
+var _action_space: Dictionary
+var _action_space_inference: Array[Dictionary] = []
+var _obs_space: Dictionary
+
+
+# Called when the node enters the scene tree for the first time.
+func _ready():
+	await get_tree().root.ready
+	get_tree().set_pause(true)
+	_initialize()
+	await get_tree().create_timer(1.0).timeout
+	get_tree().set_pause(false)
+
+
+func _initialize():
+	_get_agents()
+	args = _get_args()
+	Engine.physics_ticks_per_second = _get_speedup() * 60  # Replace with function body.
+	Engine.time_scale = _get_speedup() * 1.0
+	prints(
+		"physics ticks",
+		Engine.physics_ticks_per_second,
+		Engine.time_scale,
+		_get_speedup(),
+		speed_up
+	)
+
+	_set_heuristic("human", all_agents)
+
+	_initialize_training_agents()
+	_initialize_inference_agents()
+	_initialize_demo_recording()
+
+	_set_seed()
+	_set_action_repeat()
+	initialized = true
+
+
+func _initialize_training_agents():
+	if agents_training.size() > 0:
+		_obs_space = agents_training[0].get_obs_space()
+		_action_space = agents_training[0].get_action_space()
+		connected = connect_to_server()
+		if connected:
+			_set_heuristic("model", agents_training)
+			_handshake()
+			_send_env_info()
+		else:
+			push_warning(
+				"Couldn't connect to Python server, using human controls instead. ",
+				"Did you start the training server using e.g. `gdrl` from the console?"
+			)
+
+
+func _initialize_inference_agents():
+	if agents_inference.size() > 0:
+		if control_mode == ControlModes.ONNX_INFERENCE:
+			assert(
+				FileAccess.file_exists(onnx_model_path),
+				"Onnx Model Path set on Sync node does not exist: %s" % onnx_model_path
+			)
+			onnx_models[onnx_model_path] = ONNXModel.new(onnx_model_path, 1)
+
+		for agent in agents_inference:
+			_action_space_inference.append(agent.get_action_space())
+
+			var agent_onnx_model: ONNXModel
+			if agent.onnx_model_path.is_empty():
+				assert(
+					onnx_models.has(onnx_model_path),
+					(
+						"Node %s has no onnx model path set " % agent.get_path()
+						+ "and sync node's control mode is not set to OnnxInference. "
+						+ "Either add the path to the AIController, "
+						+ "or if you want to use the path set on sync node instead, "
+						+ "set control mode to OnnxInference."
+					)
+				)
+				prints(
+					"Info: AIController %s" % agent.get_path(),
+					"has no onnx model path set.",
+					"Using path set on the sync node instead."
+				)
+				agent_onnx_model = onnx_models[onnx_model_path]
+			else:
+				if not onnx_models.has(agent.onnx_model_path):
+					assert(
+						FileAccess.file_exists(agent.onnx_model_path),
+						(
+							"Onnx Model Path set on %s node does not exist: %s"
+							% [agent.get_path(), agent.onnx_model_path]
+						)
+					)
+					onnx_models[agent.onnx_model_path] = ONNXModel.new(agent.onnx_model_path, 1)
+				agent_onnx_model = onnx_models[agent.onnx_model_path]
+
+			agent.onnx_model = agent_onnx_model
+		_set_heuristic("model", agents_inference)
+
+
+func _initialize_demo_recording():
+	if agent_demo_record:
+		InputMap.add_action("RemoveLastDemoEpisode")
+		InputMap.action_add_event(
+			"RemoveLastDemoEpisode", agent_demo_record.remove_last_episode_key
+		)
+		current_demo_trajectory.resize(2)
+		current_demo_trajectory[0] = []
+		current_demo_trajectory[1] = []
+		agent_demo_record.heuristic = "demo_record"
+
+
+func _physics_process(_delta):
+	# two modes, human control, agent control
+	# pause tree, send obs, get actions, set actions, unpause tree
+
+	_demo_record_process()
+
+	if n_action_steps % action_repeat != 0:
+		n_action_steps += 1
+		return
+
+	n_action_steps += 1
+
+	_training_process()
+	_inference_process()
+	_heuristic_process()
+
+
+func _training_process():
+	if connected:
+		get_tree().set_pause(true)
+
+		if just_reset:
+			just_reset = false
+			var obs = _get_obs_from_agents(agents_training)
+
+			var reply = {"type": "reset", "obs": obs}
+			_send_dict_as_json_message(reply)
+			# this should go straight to getting the action and setting it checked the agent, no need to perform one phyics tick
+			get_tree().set_pause(false)
+			return
+
+		if need_to_send_obs:
+			need_to_send_obs = false
+			var reward = _get_reward_from_agents()
+			var done = _get_done_from_agents()
+			#_reset_agents_if_done() # this ensures the new observation is from the next env instance : NEEDS REFACTOR
+
+			var obs = _get_obs_from_agents(agents_training)
+
+			var reply = {"type": "step", "obs": obs, "reward": reward, "done": done}
+			_send_dict_as_json_message(reply)
+
+		var handled = handle_message()
+
+
+func _inference_process():
+	if agents_inference.size() > 0:
+		var obs: Array = _get_obs_from_agents(agents_inference)
+		var actions = []
+
+		for agent_id in range(0, agents_inference.size()):
+			var action = agents_inference[agent_id].onnx_model.run_inference(
+				obs[agent_id]["obs"], 1.0
+			)
+			action["output"] = clamp_array(action["output"], -1.0, 1.0)
+			var action_dict = _extract_action_dict(
+				action["output"], _action_space_inference[agent_id]
+			)
+			actions.append(action_dict)
+
+		_set_agent_actions(actions, agents_inference)
+		_reset_agents_if_done(agents_inference)
+		get_tree().set_pause(false)
+
+
+func _demo_record_process():
+	if not agent_demo_record:
+		return
+
+	if Input.is_action_just_pressed("RemoveLastDemoEpisode"):
+		print("[Sync script][Demo recorder] Removing last recorded episode.")
+		demo_trajectories.remove_at(demo_trajectories.size() - 1)
+		print("Remaining episode count: %d" % demo_trajectories.size())
+
+	if n_action_steps % agent_demo_record.action_repeat != 0:
+		return
+
+	var obs_dict: Dictionary = agent_demo_record.get_obs()
+
+	# Get the current obs from the agent
+	assert(
+		obs_dict.has("obs"),
+		"Demo recorder needs an 'obs' key in get_obs() returned dictionary to record obs from."
+	)
+	current_demo_trajectory[0].append(obs_dict.obs)
+
+	# Get the action applied for the current obs from the agent
+	agent_demo_record.set_action()
+	var acts = agent_demo_record.get_action()
+
+	var terminal = agent_demo_record.get_done()
+	# Record actions only for non-terminal states
+	if terminal:
+		agent_demo_record.set_done_false()
+	else:
+		current_demo_trajectory[1].append(acts)
+
+	if terminal:
+		#current_demo_trajectory[2].append(true)
+		demo_trajectories.append(current_demo_trajectory.duplicate(true))
+		print("[Sync script][Demo recorder] Recorded episode count: %d" % demo_trajectories.size())
+		current_demo_trajectory[0].clear()
+		current_demo_trajectory[1].clear()
+
+
+func _heuristic_process():
+	for agent in agents_heuristic:
+		_reset_agents_if_done(agents_heuristic)
+
+
+func _extract_action_dict(action_array: Array, action_space: Dictionary):
+	var index = 0
+	var result = {}
+	for key in action_space.keys():
+		var size = action_space[key]["size"]
+		if action_space[key]["action_type"] == "discrete":
+			result[key] = round(action_array[index])
+		else:
+			result[key] = action_array.slice(index, index + size)
+		index += size
+
+	return result
+
+
+## For AIControllers that inherit mode from sync, sets the correct mode.
+func _set_agent_mode(agent: Node):
+	var agent_inherits_mode: bool = agent.control_mode == agent.ControlModes.INHERIT_FROM_SYNC
+
+	if agent_inherits_mode:
+		match control_mode:
+			ControlModes.HUMAN:
+				agent.control_mode = agent.ControlModes.HUMAN
+			ControlModes.TRAINING:
+				agent.control_mode = agent.ControlModes.TRAINING
+			ControlModes.ONNX_INFERENCE:
+				agent.control_mode = agent.ControlModes.ONNX_INFERENCE
+
+
+func _get_agents():
+	all_agents = get_tree().get_nodes_in_group("AGENT")
+	for agent in all_agents:
+		_set_agent_mode(agent)
+
+		if agent.control_mode == agent.ControlModes.TRAINING:
+			agents_training.append(agent)
+		elif agent.control_mode == agent.ControlModes.ONNX_INFERENCE:
+			agents_inference.append(agent)
+		elif agent.control_mode == agent.ControlModes.HUMAN:
+			agents_heuristic.append(agent)
+		elif agent.control_mode == agent.ControlModes.RECORD_EXPERT_DEMOS:
+			assert(
+				not agent_demo_record,
+				"Currently only a single AIController can be used for recording expert demos."
+			)
+			agent_demo_record = agent
+
+
+func _set_heuristic(heuristic, agents: Array):
+	for agent in agents:
+		agent.set_heuristic(heuristic)
+
+
+func _handshake():
+	print("performing handshake")
+
+	var json_dict = _get_dict_json_message()
+	assert(json_dict["type"] == "handshake")
+	var major_version = json_dict["major_version"]
+	var minor_version = json_dict["minor_version"]
+	if major_version != MAJOR_VERSION:
+		print("WARNING: major verison mismatch ", major_version, " ", MAJOR_VERSION)
+	if minor_version != MINOR_VERSION:
+		print("WARNING: minor verison mismatch ", minor_version, " ", MINOR_VERSION)
+
+	print("handshake complete")
+
+
+func _get_dict_json_message():
+	# returns a dictionary from of the most recent message
+	# this is not waiting
+	while stream.get_available_bytes() == 0:
+		stream.poll()
+		if stream.get_status() != 2:
+			print("server disconnected status, closing")
+			get_tree().quit()
+			return null
+
+		OS.delay_usec(10)
+
+	var message = stream.get_string()
+	var json_data = JSON.parse_string(message)
+
+	return json_data
+
+
+func _send_dict_as_json_message(dict):
+	stream.put_string(JSON.stringify(dict, "", false))
+
+
+func _send_env_info():
+	var json_dict = _get_dict_json_message()
+	assert(json_dict["type"] == "env_info")
+
+	var message = {
+		"type": "env_info",
+		"observation_space": _obs_space,
+		"action_space": _action_space,
+		"n_agents": len(agents_training)
+	}
+	_send_dict_as_json_message(message)
+
+
+func connect_to_server():
+	print("Waiting for one second to allow server to start")
+	OS.delay_msec(1000)
+	print("trying to connect to server")
+	stream = StreamPeerTCP.new()
+
+	# "localhost" was not working on windows VM, had to use the IP
+	var ip = "127.0.0.1"
+	var port = _get_port()
+	var connect = stream.connect_to_host(ip, port)
+	stream.set_no_delay(true)  # TODO check if this improves performance or not
+	stream.poll()
+	# Fetch the status until it is either connected (2) or failed to connect (3)
+	while stream.get_status() < 2:
+		stream.poll()
+	return stream.get_status() == 2
+
+
+func _get_args():
+	print("getting command line arguments")
+	var arguments = {}
+	for argument in OS.get_cmdline_args():
+		print(argument)
+		if argument.find("=") > -1:
+			var key_value = argument.split("=")
+			arguments[key_value[0].lstrip("--")] = key_value[1]
+		else:
+			# Options without an argument will be present in the dictionary,
+			# with the value set to an empty string.
+			arguments[argument.lstrip("--")] = ""
+
+	return arguments
+
+
+func _get_speedup():
+	print(args)
+	return args.get("speedup", str(speed_up)).to_float()
+
+
+func _get_port():
+	return args.get("port", DEFAULT_PORT).to_int()
+
+
+func _set_seed():
+	var _seed = args.get("env_seed", DEFAULT_SEED).to_int()
+	seed(_seed)
+
+
+func _set_action_repeat():
+	action_repeat = args.get("action_repeat", str(action_repeat)).to_int()
+
+
+func disconnect_from_server():
+	stream.disconnect_from_host()
+
+
+func handle_message() -> bool:
+	# get json message: reset, step, close
+	var message = _get_dict_json_message()
+	if message["type"] == "close":
+		print("received close message, closing game")
+		get_tree().quit()
+		get_tree().set_pause(false)
+		return true
+
+	if message["type"] == "reset":
+		print("resetting all agents")
+		_reset_agents()
+		just_reset = true
+		get_tree().set_pause(false)
+		#print("resetting forcing draw")
+#        RenderingServer.force_draw()
+#        var obs = _get_obs_from_agents()
+#        print("obs ", obs)
+#        var reply = {
+#            "type": "reset",
+#            "obs": obs
+#        }
+#        _send_dict_as_json_message(reply)
+		return true
+
+	if message["type"] == "call":
+		var method = message["method"]
+		var returns = _call_method_on_agents(method)
+		var reply = {"type": "call", "returns": returns}
+		print("calling method from Python")
+		_send_dict_as_json_message(reply)
+		return handle_message()
+
+	if message["type"] == "action":
+		var action = message["action"]
+		_set_agent_actions(action, agents_training)
+		need_to_send_obs = true
+		get_tree().set_pause(false)
+		return true
+
+	print("message was not handled")
+	return false
+
+
+func _call_method_on_agents(method):
+	var returns = []
+	for agent in all_agents:
+		returns.append(agent.call(method))
+
+	return returns
+
+
+func _reset_agents_if_done(agents = all_agents):
+	for agent in agents:
+		if agent.get_done():
+			agent.set_done_false()
+
+
+func _reset_agents(agents = all_agents):
+	for agent in agents:
+		agent.needs_reset = true
+		#agent.reset()
+
+
+func _get_obs_from_agents(agents: Array = all_agents):
+	var obs = []
+	for agent in agents:
+		obs.append(agent.get_obs())
+	return obs
+
+
+func _get_reward_from_agents(agents: Array = agents_training):
+	var rewards = []
+	for agent in agents:
+		rewards.append(agent.get_reward())
+		agent.zero_reward()
+	return rewards
+
+
+func _get_done_from_agents(agents: Array = agents_training):
+	var dones = []
+	for agent in agents:
+		var done = agent.get_done()
+		if done:
+			agent.set_done_false()
+		dones.append(done)
+	return dones
+
+
+func _set_agent_actions(actions, agents: Array = all_agents):
+	for i in range(len(actions)):
+		agents[i].set_action(actions[i])
+
+
+func clamp_array(arr: Array, min: float, max: float):
+	var output: Array = []
+	for a in arr:
+		output.append(clamp(a, min, max))
+	return output
+
+
+## Save recorded export demos on window exit (Close window instead of "Stop" button in Godot Editor)
+func _notification(what):
+	if not agent_demo_record:
+		return
+
+	if what == NOTIFICATION_PREDELETE:
+		var json_string = JSON.stringify(demo_trajectories, "", false)
+		var file = FileAccess.open(agent_demo_record.expert_demo_save_path, FileAccess.WRITE)
+
+		if not file:
+			var error: Error = FileAccess.get_open_error()
+			assert(not error, "There was an error opening the file: %d" % error)
+
+		file.store_line(json_string)
+		var error = file.get_error()
+		assert(not error, "There was an error after trying to write to the file: %d" % error)

asset-license.md

@@ -0,0 +1,5 @@
+Robot Volleyball Environment made by Ivan-267 using Godot, Godot RL Agents, and Blender. 
+
+The following license is only for the assets (.blend files and .png files) in the folder "blender" and "sky.exr" file in the folder "textures":
+Author: https://github.com/Ivan-267
+License: https://creativecommons.org/licenses/by/4.0/

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+process/normal_map_invert_y=false
+process/hdr_as_srgb=false
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+
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+skins/use_named_skins=true
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+animation/fps=30
+animation/trimming=false
+animation/remove_immutable_tracks=true
+import_script/path=""
+_subresources={}
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+blender/nodes/punctual_lights=true
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onnx/volleyball.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:33e71a5aa6421ae9c92af5d3bd83bca25854ae46bac607ae56bb731ce219a577
+size 23502

project.godot

@@ -0,0 +1,58 @@
+; Engine configuration file.
+; It's best edited using the editor UI and not directly,
+; since the parameters that go here are not all obvious.
+;
+; Format:
+;   [section] ; section goes between []
+;   param=value ; assign values to parameters
+
+config_version=5
+
+[application]
+
+config/name="RobotVolleyball"
+run/main_scene="res://scenes/training_scene/training_scene.tscn"
+config/features=PackedStringArray("4.2", "C#", "Forward Plus")
+config/icon="res://icon.svg"
+
+[display]
+
+window/size/mode=4
+
+[dotnet]
+
+project/assembly_name="RobotVolleyball"
+
+[editor_plugins]
+
+enabled=PackedStringArray("res://addons/godot_rl_agents/plugin.cfg")
+
+[input]
+
+move_left={
+"deadzone": 0.5,
+"events": [Object(InputEventKey,"resource_local_to_scene":false,"resource_name":"","device":-1,"window_id":0,"alt_pressed":false,"shift_pressed":false,"ctrl_pressed":false,"meta_pressed":false,"pressed":false,"keycode":0,"physical_keycode":65,"key_label":0,"unicode":97,"echo":false,"script":null)
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+, Object(InputEventJoypadButton,"resource_local_to_scene":false,"resource_name":"","device":-1,"button_index":13,"pressure":0.0,"pressed":true,"script":null)
+]
+}
+move_right={
+"deadzone": 0.5,
+"events": [Object(InputEventKey,"resource_local_to_scene":false,"resource_name":"","device":-1,"window_id":0,"alt_pressed":false,"shift_pressed":false,"ctrl_pressed":false,"meta_pressed":false,"pressed":false,"keycode":0,"physical_keycode":68,"key_label":0,"unicode":100,"echo":false,"script":null)
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+, Object(InputEventJoypadButton,"resource_local_to_scene":false,"resource_name":"","device":-1,"button_index":14,"pressure":0.0,"pressed":true,"script":null)
+]
+}
+jump={
+"deadzone": 0.5,
+"events": [Object(InputEventKey,"resource_local_to_scene":false,"resource_name":"","device":-1,"window_id":0,"alt_pressed":false,"shift_pressed":false,"ctrl_pressed":false,"meta_pressed":false,"pressed":false,"keycode":0,"physical_keycode":32,"key_label":0,"unicode":32,"echo":false,"script":null)
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+, Object(InputEventKey,"resource_local_to_scene":false,"resource_name":"","device":-1,"window_id":0,"alt_pressed":false,"shift_pressed":false,"ctrl_pressed":false,"meta_pressed":false,"pressed":false,"keycode":0,"physical_keycode":4194320,"key_label":0,"unicode":0,"echo":false,"script":null)
+, Object(InputEventJoypadButton,"resource_local_to_scene":false,"resource_name":"","device":-1,"button_index":0,"pressure":0.0,"pressed":true,"script":null)
+]
+}
+
+[physics]
+
+3d/default_linear_damp=0.0
+3d/default_angular_damp=0.0

readme.md

@@ -0,0 +1,63 @@
+## Robot Volleyball
+
+A simple minigame arcade-style Volleyball example with AI vs AI and Human VS AI modes. 
+
+### Observations:
+- Position of the ball in robot's local reference,
+- Position of the ball in the robot's goal's local reference (to tell on what side of the field the ball is in),
+- Velocity of the robot in robot's local reference,
+- Velocity of the ball in robot's local reference,
+- Whether the jump sensor is colliding (which means that the robot can jump),
+- Whether the robot is serving,
+- Whether the ball has been served,
+- Hit count of the ball in a row (hitting the ball more than 2 times in a row by the same robot causes a fault),
+- How many steps has passed without hitting the ball (only counted if serving, there is a time limit in that case).
+
+### Action space:
+```gdscript
+func get_action_space() -> Dictionary:
+	return {
+		"jump": {"size": 1, "action_type": "continuous"},
+		"movement": {"size": 1, "action_type": "continuous"}
+	}
+```
+
+### Rewards:
+- Positive reward for hitting the ball once when serving,
+- Negative reward if the same robot hits the ball more than 2 times in a row,
+- Negative reward if the ball hits the robots own goal
+
+### Game over / episode end conditions:
+In infinite game mode or training mode, there are no specified end conditions.
+It's possible to disable these modes in the GameScene node in which case a winner will be announced,
+and the scores will be restarted, after a certain amount of points is reached.
+
+### Running inference:
+#### AI vs AI
+Open the scene `res://scenes/testing_scenes/ai_vs_ai.tscn` in Godot Editor, and press `F6` or click on `Run Current Scene`.
+
+#### Human vs AI
+To play VS the AI, open the scene `res://scenes/testing_scenes/human_vs_ai.tscn` in Godot Editor, and press `F6` or click on `Run Current Scene`.
+
+Controls (you can adjust them in Project Settings in Godot Editor):
+
+![Volleyball Controls](https://github.com/edbeeching/godot_rl_agents_examples/assets/61947090/26809560-815d-4d8e-b3ea-2f539a9e1fa3)
+
+### Training:
+The default scene `res://scenes/training_scene/training_scene.tscn` can be used for training.
+
+These were the parameters used to train the included onnx file (they can be applied by modifying [stable_baselines3_example.py](https://github.com/edbeeching/godot_rl_agents/blob/main/examples/stable_baselines3_example.py)):
+```python
+    policy_kwargs = dict(log_std_init=log(1.0))
+    model: PPO = PPO("MultiInputPolicy", env, verbose=1, n_epochs=10, learning_rate=0.0003, clip_range=0.2, ent_coef=0.0085, n_steps=128, batch_size=160, policy_kwargs=policy_kwargs, tensorboard_log=args.experiment_dir)
+```
+
+The arguments provided to the example for training were (feel free to adjust these):
+```bash
+--timesteps=6_500_000
+--n_parallel=5
+--speedup=15
+--env_path=[write the path to exported exe file here or remove this and n_parallel above for in-editor training]
+--onnx_export_path=volleyball.onnx
+```
+

scenes/game_scene/ball.gd

@@ -0,0 +1,34 @@
+extends RigidBody3D
+class_name Ball
+
+@export var max_velocity: float = 8.0
+
+var is_resetting: bool
+var ball_served: bool
+var new_position: Vector3
+
+
+func _integrate_forces(state):
+	state.linear_velocity = state.linear_velocity.limit_length(max_velocity)
+
+
+func reset(position_after_reset: Vector3):
+	ball_served = false
+	is_resetting = true
+	gravity_scale = 0
+	linear_velocity = Vector3.ZERO
+	angular_velocity = Vector3.ZERO
+	global_position = position_after_reset
+	new_position = position_after_reset
+
+
+func _on_body_entered(body):
+	if ball_served:
+		return
+
+	var player = body as Robot
+
+	if player and player.ai_controller.is_serving and not ball_served:
+		ball_served = true
+		player.ai_controller.is_serving = false
+		gravity_scale = 1.45

scenes/game_scene/game_manager.gd

@@ -0,0 +1,127 @@
+## TODO: Try not ending episodes, just giving the rewards.
+
+extends Node3D
+class_name GameManager
+
+@export var players: Array[Robot]
+
+## Training mode disables UI and game-resetting
+@export var training_mode: bool = true
+## Applies if the training mode is disabled
+@export var infinite_game: bool = true
+## Applies if the training mode is disabled
+@export var victory_at_score: int = 11
+@export var ui: UI
+
+var ball_hit_in_row_count: int
+var last_ball_hit_player: Robot
+
+@onready var ball = $Ball
+
+var game_just_reset: bool = false
+
+
+func get_hit_ball_count(robot: Robot):
+	if last_ball_hit_player != robot:
+		return 0.0
+	return ball_hit_in_row_count / 2.0
+
+
+func _ready():
+	for player in players:
+		player.ball = ball
+		player.game_manager = self
+	activate_game_reset()
+
+
+func _physics_process(_delta):
+	if ball.global_position.y < -2:
+		activate_game_reset()
+
+	if game_just_reset and not training_mode:
+		reset_ball(players.pick_random())
+		process_mode = Node.PROCESS_MODE_DISABLED
+		ui.show_get_ready_text(3)
+		await get_tree().create_timer(3, true, true).timeout
+		game_just_reset = false
+
+		for player in players:
+			player = player as Robot
+			player.score = 0
+			player.global_position = player.initial_position
+			player.linear_velocity = Vector3.ZERO
+
+		ball_hit_in_row_count = 0
+		process_mode = Node.PROCESS_MODE_ALWAYS
+
+
+func _on_ball_body_entered(body):
+	var player = body as Robot
+	if player:
+		if not ball.ball_served:
+			player.ai_controller.reward += 1.0
+
+		if last_ball_hit_player == player:
+			ball_hit_in_row_count += 1
+		else:
+			ball_hit_in_row_count = 1
+			last_ball_hit_player = player
+
+		if ball_hit_in_row_count > 2:
+			player.ai_controller.reward -= 1.0
+			player.other_player.score += 1
+			reset_ball(player.other_player)
+
+
+func handle_goal_hit(goal: Area3D):
+	for player in players:
+		if player.goal == goal:
+			player.ai_controller.reward -= 1.0
+			player.other_player.score += 1
+			reset_ball(player.other_player)
+
+
+func calc_ball_position(player: Robot):
+	return player.initial_position + Vector3.UP * 1.1 + Vector3.RIGHT * randf_range(-0.5, 0.5)
+
+
+func _on_goal_ball_entered(goal):
+	handle_goal_hit(goal)
+
+
+func reset_ball(player: Robot):
+	handle_victory(player)
+	ball_hit_in_row_count = 0
+	ball.ball_served = false
+	player.ai_controller.is_serving = true
+	player.other_player.ai_controller.is_serving = false
+
+	for robot in players:
+		robot.ai_controller.steps_without_ball_hit_while_serving = 0
+
+	ball.reset(calc_ball_position(player))
+
+
+func handle_victory(potential_winner: Robot):
+	if training_mode:
+		return
+
+	if check_if_game_winner(potential_winner):
+		if potential_winner.ai_controller.control_mode == AIController3D.ControlModes.HUMAN:
+			ui.show_winner_text("Player", 3)
+		else:
+			ui.show_winner_text(potential_winner.name, 3)
+		activate_game_reset()
+
+
+func check_if_game_winner(robot: Robot):
+	if infinite_game:
+		return
+
+	if robot.score >= victory_at_score:
+		return true
+	return false
+
+
+func activate_game_reset():
+	game_just_reset = true

scenes/game_scene/game_scene.tscn

@@ -0,0 +1,377 @@
+[gd_scene load_steps=36 format=3 uid="uid://2x5filmso32a"]
+
+[ext_resource type="Script" path="res://scenes/game_scene/game_manager.gd" id="1_g4wvl"]
+[ext_resource type="Script" path="res://scenes/game_scene/goal.gd" id="2_ypwf4"]
+[ext_resource type="PackedScene" uid="uid://3gt386v3b1ej" path="res://scenes/robot/robot.tscn" id="3_y4pr4"]
+[ext_resource type="Shader" uid="uid://der346ayua0mt" path="res://shaders/sway.tres" id="4_dl2k6"]
+[ext_resource type="PackedScene" uid="uid://bfwxkao4uhtdy" path="res://blender/static_decorations.blend" id="5_5d1h1"]
+[ext_resource type="Texture2D" uid="uid://7pxcpp75142j" path="res://blender/grass.png" id="5_kw2hd"]
+[ext_resource type="Script" path="res://scenes/game_scene/ball.gd" id="5_xjnfp"]
+[ext_resource type="Texture2D" uid="uid://cc7p54wl68umy" path="res://blender/grass2.png" id="6_oqveb"]
+[ext_resource type="Texture2D" uid="uid://bqnr3brm7i4i7" path="res://blender/grass3.png" id="7_h2k6l"]
+
+[sub_resource type="Gradient" id="Gradient_0rod8"]
+colors = PackedColorArray(0.683594, 0.683594, 0.683594, 1, 1, 1, 1, 1)
+
+[sub_resource type="FastNoiseLite" id="FastNoiseLite_kgiw2"]
+frequency = 0.1
+
+[sub_resource type="NoiseTexture2D" id="NoiseTexture2D_i1j55"]
+color_ramp = SubResource("Gradient_0rod8")
+noise = SubResource("FastNoiseLite_kgiw2")
+
+[sub_resource type="StandardMaterial3D" id="StandardMaterial3D_si1yy"]
+albedo_color = Color(0.729412, 0.670588, 0.0352941, 1)
+albedo_texture = SubResource("NoiseTexture2D_i1j55")
+
+[sub_resource type="StandardMaterial3D" id="StandardMaterial3D_v4edw"]
+albedo_color = Color(1, 0.6, 0, 1)
+
+[sub_resource type="StandardMaterial3D" id="StandardMaterial3D_tsppj"]
+transparency = 1
+albedo_color = Color(0.0156863, 0.627451, 0.721569, 0.211765)
+metallic = 0.85
+roughness = 0.0
+
+[sub_resource type="BoxShape3D" id="BoxShape3D_20bsr"]
+size = Vector3(10, 10, 5)
+
+[sub_resource type="BoxShape3D" id="BoxShape3D_nucsm"]
+size = Vector3(10, 10, 5)
+
+[sub_resource type="BoxShape3D" id="BoxShape3D_7q7xm"]
+size = Vector3(30, 20, 15)
+
+[sub_resource type="BoxShape3D" id="BoxShape3D_u865r"]
+size = Vector3(0.2, 1.162, 2)
+
+[sub_resource type="CylinderShape3D" id="CylinderShape3D_ho4bs"]
+radius = 0.1
+
+[sub_resource type="BoxShape3D" id="BoxShape3D_efmyo"]
+size = Vector3(3, 1, 2)
+
+[sub_resource type="Gradient" id="Gradient_epedm"]
+colors = PackedColorArray(0.811765, 0.811765, 0.811765, 1, 1, 1, 1, 1)
+
+[sub_resource type="FastNoiseLite" id="FastNoiseLite_quakn"]
+fractal_octaves = 4
+fractal_lacunarity = 7.155
+fractal_gain = 0.415
+fractal_weighted_strength = 0.5
+
+[sub_resource type="NoiseTexture2D" id="NoiseTexture2D_vmvmy"]
+width = 1280
+height = 1280
+color_ramp = SubResource("Gradient_epedm")
+noise = SubResource("FastNoiseLite_quakn")
+
+[sub_resource type="StandardMaterial3D" id="StandardMaterial3D_unsk7"]
+albedo_color = Color(0, 0.517647, 0.0235294, 1)
+albedo_texture = SubResource("NoiseTexture2D_vmvmy")
+
+[sub_resource type="PlaneMesh" id="PlaneMesh_o0rtt"]
+size = Vector2(20, 20)
+
+[sub_resource type="ShaderMaterial" id="ShaderMaterial_ie7u0"]
+resource_name = "grass"
+render_priority = 0
+shader = ExtResource("4_dl2k6")
+shader_parameter/albedo = Color(1, 1, 1, 1)
+shader_parameter/point_size = 1.0
+shader_parameter/roughness = 0.5
+shader_parameter/metallic_texture_channel = null
+shader_parameter/specular = 0.5
+shader_parameter/metallic = 0.0
+shader_parameter/uv1_scale = Vector3(1, 1, 1)
+shader_parameter/uv1_offset = Vector3(0, 0, 0)
+shader_parameter/uv2_scale = Vector3(1, 1, 1)
+shader_parameter/uv2_offset = Vector3(0, 0, 0)
+shader_parameter/sway_frequency = 1.0
+shader_parameter/sway_scale = 0.1
+shader_parameter/texture_albedo = ExtResource("5_kw2hd")
+
+[sub_resource type="ShaderMaterial" id="ShaderMaterial_pa0jt"]
+resource_name = "grass2"
+render_priority = 0
+shader = ExtResource("4_dl2k6")
+shader_parameter/albedo = Color(1, 1, 1, 1)
+shader_parameter/point_size = 1.0
+shader_parameter/roughness = 0.5
+shader_parameter/metallic_texture_channel = null
+shader_parameter/specular = 0.5
+shader_parameter/metallic = 0.0
+shader_parameter/uv1_scale = Vector3(1, 1, 1)
+shader_parameter/uv1_offset = Vector3(0, 0, 0)
+shader_parameter/uv2_scale = Vector3(1, 1, 1)
+shader_parameter/uv2_offset = Vector3(0, 0, 0)
+shader_parameter/sway_frequency = 1.0
+shader_parameter/sway_scale = 0.1
+shader_parameter/texture_albedo = ExtResource("6_oqveb")
+
+[sub_resource type="ShaderMaterial" id="ShaderMaterial_og20f"]
+resource_name = "grass3"
+render_priority = 0
+shader = ExtResource("4_dl2k6")
+shader_parameter/albedo = Color(1, 1, 1, 1)
+shader_parameter/point_size = 1.0
+shader_parameter/roughness = 0.5
+shader_parameter/metallic_texture_channel = null
+shader_parameter/specular = 0.5
+shader_parameter/metallic = 0.0
+shader_parameter/uv1_scale = Vector3(1, 1, 1)
+shader_parameter/uv1_offset = Vector3(0, 0, 0)
+shader_parameter/uv2_scale = Vector3(1, 1, 1)
+shader_parameter/uv2_offset = Vector3(0, 0, 0)
+shader_parameter/sway_frequency = 1.0
+shader_parameter/sway_scale = 0.1
+shader_parameter/texture_albedo = ExtResource("7_h2k6l")
+
+[sub_resource type="ArrayMesh" id="ArrayMesh_askja"]
+_surfaces = [{
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+size = Vector3(2.1, 4, 0.1)
+
+[node name="Wall3" type="CSGBox3D" parent="Field"]
+transform = Transform3D(-4.37114e-08, 1, 4.37114e-08, 0, -4.37114e-08, 1, 1, 4.37114e-08, 0, 8.74228e-08, 3.95, 0)
+material_override = SubResource("StandardMaterial3D_tsppj")
+size = Vector3(2.1, 6.1, 0.1)
+
+[node name="FieldColliders" type="StaticBody3D" parent="Field"]
+
+[node name="CollisionShape3D" type="CollisionShape3D" parent="Field/FieldColliders"]
+transform = Transform3D(1, 0, 0, 0, 1, 0, 0, 0, 1, -8, 0.5, 0)
+shape = SubResource("BoxShape3D_20bsr")
+
+[node name="CollisionShape3D2" type="CollisionShape3D" parent="Field/FieldColliders"]
+transform = Transform3D(1, 0, 0, 0, 1, 0, 0, 0, 1, 8, 1.17, 0)
+shape = SubResource("BoxShape3D_nucsm")
+
+[node name="CollisionShape3D5" type="CollisionShape3D" parent="Field/FieldColliders"]
+transform = Transform3D(1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 14, 0)
+shape = SubResource("BoxShape3D_7q7xm")
+
+[node name="CollisionShape3D6" type="CollisionShape3D" parent="Field/FieldColliders"]
+transform = Transform3D(1, 0, 0, 0, 1, 0, 0, 0, 1, 0, -10, 0)
+shape = SubResource("BoxShape3D_7q7xm")
+
+[node name="CollisionShape3D7" type="CollisionShape3D" parent="Field/FieldColliders"]
+transform = Transform3D(1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0.418945, 0)
+shape = SubResource("BoxShape3D_u865r")
+
+[node name="CollisionShape3D8" type="CollisionShape3D" parent="Field/FieldColliders"]
+transform = Transform3D(1.91069e-15, 4.37114e-08, 1, 1, -4.37114e-08, 0, 4.37114e-08, 1, -4.37114e-08, 0, 0.9985, 0)
+shape = SubResource("CylinderShape3D_ho4bs")
+
+[node name="Goal" type="Area3D" parent="Field"]
+process_mode = 1
+collision_layer = 0
+collision_mask = 4
+monitorable = false
+script = ExtResource("2_ypwf4")
+
+[node name="CollisionShape3D" type="CollisionShape3D" parent="Field/Goal"]
+transform = Transform3D(1, 0, 0, 0, 1, 0, 0, 0, 1, -1.5, -0.4, 0)
+shape = SubResource("BoxShape3D_efmyo")
+
+[node name="Goal2" type="Area3D" parent="Field"]
+process_mode = 1
+transform = Transform3D(-1, 0, -8.74228e-08, 0, 1, 0, 8.74228e-08, 0, -1, 0, 0, 0)
+collision_layer = 0
+collision_mask = 4
+monitorable = false
+script = ExtResource("2_ypwf4")
+
+[node name="CollisionShape3D" type="CollisionShape3D" parent="Field/Goal2"]
+transform = Transform3D(1, 0, 0, 0, 1, 0, 0, 0, 1, -1.5, -0.4, 0)
+shape = SubResource("BoxShape3D_efmyo")
+
+[node name="Decorations" type="Node3D" parent="Field"]
+transform = Transform3D(1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0.0384959, 0)
+
+[node name="MeshInstance3D" type="MeshInstance3D" parent="Field/Decorations"]
+transform = Transform3D(1, 0, 0, 0, 1, 0, 0, 0, 1, 0, -0.0506201, 0)
+material_override = SubResource("StandardMaterial3D_unsk7")
+mesh = SubResource("PlaneMesh_o0rtt")
+skeleton = NodePath("../../..")
+
+[node name="static_decorations" parent="Field/Decorations" instance=ExtResource("5_5d1h1")]
+transform = Transform3D(-4.37114e-08, 0, 1, 0, 1, 0, -1, 0, -4.37114e-08, 0, 0, 0)
+
+[node name="grass" parent="Field/Decorations/static_decorations" index="2"]
+mesh = SubResource("ArrayMesh_1dxiy")
+
+[node name="Robot" parent="." node_paths=PackedStringArray("other_player", "goal") instance=ExtResource("3_y4pr4")]
+transform = Transform3D(-4.37114e-08, 0, -1, 0, 1, 0, 1, 0, -4.37114e-08, -1.5, 0.2, 0)
+other_player = NodePath("../Robot2")
+goal = NodePath("../Field/Goal")
+
+[node name="Robot2" parent="." node_paths=PackedStringArray("other_player", "goal") instance=ExtResource("3_y4pr4")]
+transform = Transform3D(-4.37114e-08, 0, 1, 0, 1, 0, -1, 0, -4.37114e-08, 1.5, 0.2, 0)
+other_player = NodePath("../Robot")
+goal = NodePath("../Field/Goal2")
+
+[node name="Ball" type="RigidBody3D" parent="."]
+transform = Transform3D(1, 0, 0, 0, 1, 0, 0, 0, 1, -0.560747, 1.45254, 0)
+collision_layer = 4
+collision_mask = 3
+axis_lock_linear_z = true
+mass = 0.1
+physics_material_override = SubResource("PhysicsMaterial_7eqbu")
+gravity_scale = 1.25
+sleeping = true
+lock_rotation = true
+continuous_cd = true
+max_contacts_reported = 1
+contact_monitor = true
+linear_damp_mode = 1
+angular_damp_mode = 1
+script = ExtResource("5_xjnfp")
+max_velocity = 9.0
+
+[node name="MeshInstance3D" type="MeshInstance3D" parent="Ball"]
+material_override = SubResource("StandardMaterial3D_5bx48")
+mesh = SubResource("SphereMesh_3ng7x")
+
+[node name="CollisionShape3D" type="CollisionShape3D" parent="Ball"]
+shape = SubResource("SphereShape3D_sg3a8")
+
+[connection signal="ball_entered" from="Field/Goal" to="." method="_on_goal_ball_entered"]
+[connection signal="ball_entered" from="Field/Goal2" to="." method="_on_goal_ball_entered"]
+[connection signal="body_entered" from="Ball" to="." method="_on_ball_body_entered"]
+[connection signal="body_entered" from="Ball" to="Ball" method="_on_body_entered"]
+
+[editable path="Field/Decorations/static_decorations"]

scenes/game_scene/goal.gd

@@ -0,0 +1,13 @@
+extends Area3D
+class_name Goal
+
+signal ball_entered(goal: Goal)
+
+
+func _ready():
+	connect("body_entered", on_body_entered)
+
+
+func on_body_entered(body):
+	if body is Ball:
+		ball_entered.emit(self)

scenes/robot/RobotAIController.gd

@@ -0,0 +1,79 @@
+extends AIController3D
+class_name RobotAIController
+
+@onready var robot: Robot = get_parent()
+@onready var sensors: Array[Node] = $"../Sensors".get_children()
+
+var steps_without_ball_hit_while_serving: int
+var is_serving: bool
+
+
+func reset():
+	super.reset()
+
+
+func _physics_process(_delta):
+	n_steps += 1
+
+	if is_serving:
+		steps_without_ball_hit_while_serving += 1
+
+	if steps_without_ball_hit_while_serving > 400:
+		reward -= 1
+		robot.other_player.score += 1
+		robot.game_manager.reset_ball(robot.other_player)
+
+
+func get_obs() -> Dictionary:
+	var ball_position = robot.to_local(robot.ball.global_position) / 8.0
+	var ball_goal_position = robot.goal.to_local(robot.ball.global_position) / 5.0
+	var robot_velocity = (
+		(robot.global_transform.basis.inverse() * robot.linear_velocity.limit_length(10.0)) / 10.0
+	)
+	var ball_velocity = (
+		(robot.global_transform.basis.inverse() * robot.ball.linear_velocity.limit_length(8.0))
+		/ 8.0
+	)
+
+	var observations: Array[float] = [
+		ball_position.y,
+		ball_position.z,
+		ball_goal_position.x,
+		ball_goal_position.z,
+		robot_velocity.y,
+		robot_velocity.z,
+		ball_velocity.y,
+		ball_velocity.z,
+		float(robot.jump_sensor.is_colliding()),
+		float(float(is_serving)),
+		float(robot.ball.ball_served),
+		robot.game_manager.get_hit_ball_count(robot),
+		steps_without_ball_hit_while_serving / 400.0
+	]
+
+	observations.append_array(get_raycast_sensor_obs())
+
+	return {"obs": observations}
+
+
+func get_reward() -> float:
+	return reward
+
+
+func get_action_space() -> Dictionary:
+	return {
+		"jump": {"size": 1, "action_type": "continuous"},
+		"movement": {"size": 1, "action_type": "continuous"}
+	}
+
+
+func set_action(action) -> void:
+	robot.requested_movement = clamp(action.movement[0], -1.0, 1.0)
+	robot.jump_requested = action.jump[0] > 0
+
+
+func get_raycast_sensor_obs():
+	var all_raycast_sensor_obs: Array[float] = []
+	for raycast_sensor in sensors:
+		all_raycast_sensor_obs.append_array(raycast_sensor.get_observation())
+	return all_raycast_sensor_obs

scenes/robot/robot.gd

@@ -0,0 +1,131 @@
+extends RigidBody3D
+class_name Robot
+
+@export var other_player: Robot
+@export var goal: Area3D
+
+@export var wheels: Array[Node3D]
+
+@export var acceleration: float = 100.0
+@export var rotation_speed: float = 15.0
+@export var jump_force: float = 290
+
+@export var gravity := Vector3.DOWN * 60.0
+
+@onready var robot_visual: Node3D = $robot
+@onready var jump_sensor: RayCast3D = $JumpSensor
+@onready var ai_controller: RobotAIController = $AIController3D
+@onready var particles: GPUParticles3D = $GPUParticles3D
+@onready var initial_position = global_position
+
+var game_manager: GameManager
+var ball: Ball
+
+var requested_movement: float
+var jump_requested: bool
+var velocity: Vector3
+var last_movement_direction: float = 1
+
+## If set, the score from this Robot will be displayed in the label
+var score_label: Label
+var score : int:
+	set(value):
+		score = value
+		if score_label:
+			score_label.text = str(value)
+
+
+func _ready():
+	score = 0
+	reset()
+
+
+func reset():
+	linear_velocity = Vector3.ZERO
+	global_position = initial_position
+
+
+func _physics_process(delta):
+	reset_on_needs_reset()
+	handle_movement(delta)
+
+
+func reset_on_needs_reset():
+	if ai_controller.needs_reset:
+		reset()
+		ai_controller.reset()
+	pass
+
+
+func handle_movement(delta):
+	var movement := Vector3()
+
+	if ai_controller.heuristic == "human":
+		if Input.is_action_pressed("move_left"):
+			movement.x = -1
+		if Input.is_action_pressed("move_right"):
+			movement.x = 1
+		if Input.is_action_pressed("jump"):
+			if jump_sensor.is_colliding():
+				apply_force(Vector3.UP * jump_force)
+	else:
+		movement = global_basis.z * requested_movement
+		if jump_sensor.is_colliding() and jump_requested:
+			apply_force(Vector3.UP * jump_force)
+
+	if movement:
+		last_movement_direction = sign(movement.x)
+		apply_acceleration(movement.normalized())
+
+	update_particle_effects()
+	update_wheels_and_visual_rotation(delta)
+	rotate_toward_movement(delta)
+	apply_gravity()
+
+
+func apply_acceleration(direction):
+	apply_force(direction * acceleration)
+
+
+func apply_gravity():
+	apply_force(gravity)
+
+
+func rotate_toward_movement(delta):
+	if abs(last_movement_direction) > 0.005:
+		robot_visual.global_transform = (
+			robot_visual
+			. global_transform
+			. interpolate_with(
+				robot_visual.global_transform.looking_at(
+					(
+						robot_visual.global_transform.origin
+						+ Vector3(last_movement_direction, 0, +0.01)
+					)
+				),
+				rotation_speed * delta
+			)
+			. orthonormalized()
+		)
+
+
+func update_wheels_and_visual_rotation(delta):
+	var abs_movement = abs(linear_velocity.x)
+
+	for wheel in wheels:
+		wheel.rotate_object_local(Vector3.LEFT, abs_movement * 1.3 * delta)
+	robot_visual.rotation.x = -0.01 * abs_movement
+
+
+func update_particle_effects():
+	if linear_velocity.x > 0.25 and jump_sensor.is_colliding():
+		particles.emitting = true
+	else:
+		particles.emitting = false
+
+
+func end_episode(reward: float):
+	ai_controller.reward += reward
+	ai_controller.done = true
+	ai_controller.needs_reset = true
+	pass