Model trained with PPO on LunarLander-v2 for the DEEP RL huggingface course

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+replay.mp4 filter=lfs diff=lfs merge=lfs -text

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: 281.91 +/- 14.35
+      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 0x7fe750cfa050>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fe750cfa0e0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fe750cfa170>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fe750cfa200>", "_build": "<function ActorCriticPolicy._build at 0x7fe750cfa290>", "forward": "<function ActorCriticPolicy.forward at 0x7fe750cfa320>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fe750cfa3b0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fe750cfa440>", "_predict": "<function ActorCriticPolicy._predict at 0x7fe750cfa4d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fe750cfa560>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fe750cfa5f0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fe750cfa680>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fe750cf5dc0>"}, "verbose": 0, "policy_kwargs": {}, "num_timesteps": 1001472, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1754414030890836567, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": null, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdQAAAAAAAACME251bXB5Ll9jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWAQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksBhZSMAUOUdJRSlC4="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.0014719999999999178, "_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": 4951, "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:": "gAWV/gAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFm51bXB5Ll9jb3JlLm11bHRpYXJyYXmUjAZzY2FsYXKUk5SMBW51bXB5lIwFZHR5cGWUk5SMAmk4lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGJDCAQAAAAAAAAAlIaUUpSMBXN0YXJ0lGgIaA5DCAAAAAAAAAAAlIaUUpSMBl9zaGFwZZQpjAVkdHlwZZRoC4wCaTiUiYiHlFKUKEsDaA9OTk5K/////0r/////SwB0lGKMCl9ucF9yYW5kb22UTnViLg==", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 1, "n_steps": 2048, "gamma": 0.99, "gae_lambda": 0.95, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 10, "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-5.15.167.4-microsoft-standard-WSL2-x86_64-with-glibc2.39 # 1 SMP Tue Nov 5 00:21:55 UTC 2024", "Python": "3.10.18", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.7.1+cu126", "GPU Enabled": "True", "Numpy": "2.2.6", "Cloudpickle": "3.1.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.26.2"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eca97d55e531ab9ab0596b362ee43cc0dc207c7b9ec97e1650cea114fa84c733
+size 148557

ppo-LunarLander-v2/_stable_baselines3_version

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

ppo-LunarLander-v2/data

@@ -0,0 +1,96 @@
+{
+    "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 0x7fe750cfa050>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fe750cfa0e0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fe750cfa170>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fe750cfa200>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fe750cfa290>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fe750cfa320>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fe750cfa3b0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fe750cfa440>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fe750cfa4d0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fe750cfa560>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fe750cfa5f0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fe750cfa680>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7fe750cf5dc0>"
+    },
+    "verbose": 0,
+    "policy_kwargs": {},
+    "num_timesteps": 1001472,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1754414030890836567,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "_last_obs": null,
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdQAAAAAAAACME251bXB5Ll9jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWAQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksBhZSMAUOUdJRSlC4="
+    },
+    "_last_original_obs": null,
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": -0.0014719999999999178,
+    "_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": 4951,
+    "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:": "gAWV/gAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFm51bXB5Ll9jb3JlLm11bHRpYXJyYXmUjAZzY2FsYXKUk5SMBW51bXB5lIwFZHR5cGWUk5SMAmk4lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGJDCAQAAAAAAAAAlIaUUpSMBXN0YXJ0lGgIaA5DCAAAAAAAAAAAlIaUUpSMBl9zaGFwZZQpjAVkdHlwZZRoC4wCaTiUiYiHlFKUKEsDaA9OTk5K/////0r/////SwB0lGKMCl9ucF9yYW5kb22UTnViLg==",
+        "n": "4",
+        "start": "0",
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 1,
+    "n_steps": 2048,
+    "gamma": 0.99,
+    "gae_lambda": 0.95,
+    "ent_coef": 0.0,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 64,
+    "n_epochs": 10,
+    "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 sha256:05c51fdd86e843641dc8d4f7dd8e4f50152af8b88a25afd54a4050c8aa057db6
+size 88887

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b74febd42db7e128bc21bab6ffbbc4afe60932c8f4250deffd099f1a92356337
+size 44095

ppo-LunarLander-v2/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:07c7431cf6005e7d8f367d79e995f63e2f9b981a37e3437b795d058f9af4308b
+size 1261

ppo-LunarLander-v2/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-5.15.167.4-microsoft-standard-WSL2-x86_64-with-glibc2.39 # 1 SMP Tue Nov 5 00:21:55 UTC 2024
+- Python: 3.10.18
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.7.1+cu126
+- GPU Enabled: True
+- Numpy: 2.2.6
+- Cloudpickle: 3.1.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.26.2

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:355dc32f99eb78ba82843209e893803e98ea0f6867af05da1f22cfd962392d1e
+size 124871

results.json

@@ -0,0 +1 @@
+{"mean_reward": 281.9089014, "std_reward": 14.353749937004906, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2025-08-05T21:16:50.351617"}