Upload PPO LunarLander-v2 trained agent

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+    metrics:
+    - type: mean_reward
+      value: 248.23 +/- 17.94
+      name: mean_reward
+      verified: false
+---
+
+# **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).
+
+## Usage (with Stable-baselines3)
+TODO: Add your code
+
+
+```python
+from stable_baselines3 import ...
+from huggingface_sb3 import load_from_hub
+
+...
+```

config.json

@@ -0,0 +1 @@
+{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__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    ", "__init__": "<function ActorCriticPolicy.__init__ at 0x7b0837e4fac0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7b0837e4fb50>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7b0837e4fbe0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7b0837e4fc70>", "_build": "<function ActorCriticPolicy._build at 0x7b0837e4fd00>", "forward": "<function ActorCriticPolicy.forward at 0x7b0837e4fd90>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7b0837e4fe20>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7b0837e4feb0>", "_predict": "<function ActorCriticPolicy._predict at 0x7b0837e4ff40>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7b0837e58040>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7b0837e580d0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7b0837e58160>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7b07d9bf8f40>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1733598481392494139, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.015808000000000044, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 248, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "gAWVdgIAAAAAAACMFGd5bW5hc2l1bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMDWJvdW5kZWRfYmVsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWCAAAAAAAAAABAQEBAQEBAZRoCIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksIhZSMAUOUdJRSlIwNYm91bmRlZF9hYm92ZZRoESiWCAAAAAAAAAABAQEBAQEBAZRoFUsIhZRoGXSUUpSMBl9zaGFwZZRLCIWUjANsb3eUaBEoliAAAAAAAAAAAAC0wgAAtMIAAKDAAACgwNsPScAAAKDAAAAAgAAAAICUaAtLCIWUaBl0lFKUjARoaWdolGgRKJYgAAAAAAAAAAAAtEIAALRCAACgQAAAoEDbD0lAAACgQAAAgD8AAIA/lGgLSwiFlGgZdJRSlIwIbG93X3JlcHKUjFtbLTkwLiAgICAgICAgLTkwLiAgICAgICAgIC01LiAgICAgICAgIC01LiAgICAgICAgIC0zLjE0MTU5MjcgIC01LgogIC0wLiAgICAgICAgIC0wLiAgICAgICBdlIwJaGlnaF9yZXBylIxTWzkwLiAgICAgICAgOTAuICAgICAgICAgNS4gICAgICAgICA1LiAgICAgICAgIDMuMTQxNTkyNyAgNS4KICAxLiAgICAgICAgIDEuICAgICAgIF2UjApfbnBfcmFuZG9tlE51Yi4=", "dtype": "float32", "bounded_below": "[ True  True  True  True  True  True  True  True]", "bounded_above": "[ True  True  True  True  True  True  True  True]", "_shape": [8], "low": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]", "high": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]", "low_repr": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]", "high_repr": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]", "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "gAWV2wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlE51Yi4=", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "n_steps": 1024, "gamma": 0.999, "gae_lambda": 0.98, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 4, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Thu Jun 27 21:05:47 UTC 2024", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.5.1+cu121", "GPU Enabled": "True", "Numpy": "1.26.4", "Cloudpickle": "3.1.0", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c3c2420d56fe376a1dd0924c9a29369f7b35aec6906692b624c09fa9a4831277
+size 148020

ppo-LunarLander-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+2.0.0a5

ppo-LunarLander-v2/data

@@ -0,0 +1,99 @@
+{
+    "policy_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
+        "__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    ",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7b0837e4fac0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7b0837e4fb50>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7b0837e4fbe0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7b0837e4fc70>",
+        "_build": "<function ActorCriticPolicy._build at 0x7b0837e4fd00>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7b0837e4fd90>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7b0837e4fe20>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7b0837e4feb0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7b0837e4ff40>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7b0837e58040>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7b0837e580d0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7b0837e58160>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7b07d9bf8f40>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1733598481392494139,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "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"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": null,
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": -0.015808000000000044,
+    "_stats_window_size": 100,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 248,
+    "observation_space": {
+        ":type:": "<class 'gymnasium.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "bounded_below": "[ True  True  True  True  True  True  True  True]",
+        "bounded_above": "[ True  True  True  True  True  True  True  True]",
+        "_shape": [
+            8
+        ],
+        "low": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]",
+        "high": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]",
+        "low_repr": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]",
+        "high_repr": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]",
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gymnasium.spaces.discrete.Discrete'>",
+        ":serialized:": "gAWV2wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlE51Yi4=",
+        "n": "4",
+        "start": "0",
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 16,
+    "n_steps": 1024,
+    "gamma": 0.999,
+    "gae_lambda": 0.98,
+    "ent_coef": 0.01,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 64,
+    "n_epochs": 4,
+    "clip_range": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    }
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ee916427b8d30ff414c6a49e1f485a14b10135d370a5946a6d50b15a4d7e1c04
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:721c88cd288b0c465e0cb6b5c2848485d2460f677d423a6d3cfb251db63d8a82
+size 43762

ppo-LunarLander-v2/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0c35cea3b2e60fb5e7e162d3592df775cd400e575a31c72f359fb9e654ab00c5
+size 864

ppo-LunarLander-v2/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Thu Jun 27 21:05:47 UTC 2024
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.5.1+cu121
+- GPU Enabled: True
+- Numpy: 1.26.4
+- Cloudpickle: 3.1.0
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

@@ -0,0 +1 @@
+{"mean_reward": 248.23290409999998, "std_reward": 17.940420002267263, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-12-07T19:39:35.741422"}