Upload PPO LunarLander-v2 trained agent

README.md

@@ -1,11 +1,10 @@
 ---
+library_name: stable-baselines3
 tags:
 - LunarLander-v2
-- ppo
 - deep-reinforcement-learning
 - reinforcement-learning
-- custom-implementation
-- deep-rl-course
+- stable-baselines3
 model-index:
 - name: PPO
   results:
@@ -17,14 +16,22 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 126.53 +/- 128.23
+      value: 259.30 +/- 24.14
       name: mean_reward
       verified: false
 ---
 
-  # PPO Agent Playing LunarLander-v2
+# **PPO** Agent playing **LunarLander-v2**
+This is a trained model of a **PPO** agent playing **LunarLander-v2**
+using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
 
-  This is a trained model of a PPO agent playing LunarLander-v2.
+## Usage (with Stable-baselines3)
+TODO: Add your code
 
-  # Hyperparameters
-  
+
+```python
+from stable_baselines3 import ...
+from huggingface_sb3 import load_from_hub
+
+...
+```

config.json

@@ -1 +1 @@
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It allows to keep variance\n        above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n    :param squash_output: Whether to squash the output using a tanh function,\n        this allows to ensure boundaries when using gSDE.\n    :param features_extractor_class: Features extractor to use.\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\n    :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\n    :param normalize_images: Whether to normalize images or not,\n         dividing by 255.0 (True by default)\n    :param optimizer_class: The optimizer to use,\n        ``th.optim.Adam`` by default\n    :param optimizer_kwargs: Additional keyword arguments,\n        excluding the learning rate, to pass to the optimizer\n    ", "__init__": "<function ActorCriticPolicy.__init__ at 0x7f82c65d2790>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f82c65d2820>", "reset_noise": 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ppo-LunarLander-v2.zip

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 size 147557

ppo-LunarLander-v2/data

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         "__module__": "stable_baselines3.common.policies",
         "__doc__": "\n    Policy class for actor-critic algorithms (has both policy and value prediction).\n    Used by A2C, PPO and the likes.\n\n    :param observation_space: Observation space\n    :param action_space: Action space\n    :param lr_schedule: Learning rate schedule (could be constant)\n    :param net_arch: The specification of the policy and value networks.\n    :param activation_fn: Activation function\n    :param ortho_init: Whether to use or not orthogonal initialization\n    :param use_sde: Whether to use State Dependent Exploration or not\n    :param log_std_init: Initial value for the log standard deviation\n    :param full_std: Whether to use (n_features x n_actions) parameters\n        for the std instead of only (n_features,) when using gSDE\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n        a positive standard deviation (cf paper). It allows to keep variance\n        above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n    :param squash_output: Whether to squash the output using a tanh function,\n        this allows to ensure boundaries when using gSDE.\n    :param features_extractor_class: Features extractor to use.\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\n    :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\n    :param normalize_images: Whether to normalize images or not,\n         dividing by 255.0 (True by default)\n    :param optimizer_class: The optimizer to use,\n        ``th.optim.Adam`` by default\n    :param optimizer_kwargs: Additional keyword arguments,\n        excluding the learning rate, to pass to the optimizer\n    ",
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-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f82c65d28b0>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f82c65d2940>",
-        "_build": "<function ActorCriticPolicy._build at 0x7f82c65d29d0>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7f82c65d2a60>",
-        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f82c65d2af0>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f82c65d2b80>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7f82c65d2c10>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f82c65d2ca0>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f82c65d2d30>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f82c65d2dc0>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7f5f3b14a700>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f5f3b14a790>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f5f3b14a820>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f5f3b14a8b0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f5f3b14a940>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f5f3b14a9d0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f5f3b14aa60>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f5f3b14aaf0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f5f3b14ab80>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f5f3b14ac10>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f5f3b14aca0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f5f3b14ad30>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x7f82c65caa20>"
+        "_abc_impl": "<_abc_data object at 0x7f5f3b146780>"
     },
     "verbose": 1,
     "policy_kwargs": {},
@@ -48,7 +48,7 @@
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     "seed": null,
     "action_noise": null,
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+    "start_time": 1684951338260018437,
     "learning_rate": 0.0003,
     "tensorboard_log": null,
     "lr_schedule": {
@@ -57,7 +57,7 @@
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ppo-LunarLander-v2/policy.pth

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ppo-LunarLander-v2/system_info.txt

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-- OS: Linux-5.15.0-56-generic-x86_64-with-glibc2.29 # 62~20.04.1-Ubuntu SMP Tue Nov 22 21:24:20 UTC 2022
+- OS: Linux-5.15.0-69-generic-x86_64-with-glibc2.29 # 76~20.04.1-Ubuntu SMP Mon Mar 20 15:54:19 UTC 2023
 - Python: 3.8.10
 - Stable-Baselines3: 1.7.0
 - PyTorch: 2.0.0+cu117

results.json

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