initial commit

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: 254.20 +/- 17.55
+      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 0x78f1f1b416c0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x78f1f1b41750>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x78f1f1b417e0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x78f1f1b41870>", "_build": "<function ActorCriticPolicy._build at 0x78f1f1b41900>", "forward": "<function ActorCriticPolicy.forward at 0x78f1f1b41990>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x78f1f1b41a20>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x78f1f1b41ab0>", "_predict": "<function ActorCriticPolicy._predict at 0x78f1f1b41b40>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x78f1f1b41bd0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x78f1f1b41c60>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x78f1f1b41cf0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x78f1f1b4c8c0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1702032254271635819, "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:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=", "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-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.1.0+cu118", "GPU Enabled": "True", "Numpy": "1.23.5", "Cloudpickle": "2.2.1", "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 sha256:903a1f7d2a6005b6b68cd132d81fcba35e6088e68798be766e5fc55f8b2684e1
+size 148050

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 0x78f1f1b416c0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x78f1f1b41750>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x78f1f1b417e0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x78f1f1b41870>",
+        "_build": "<function ActorCriticPolicy._build at 0x78f1f1b41900>",
+        "forward": "<function ActorCriticPolicy.forward at 0x78f1f1b41990>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x78f1f1b41a20>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x78f1f1b41ab0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x78f1f1b41b40>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x78f1f1b41bd0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x78f1f1b41c60>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x78f1f1b41cf0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x78f1f1b4c8c0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1702032254271635819,
+    "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:": "gAWVcAIAAAAAAACMFGd5bW5hc2l1bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMDWJvdW5kZWRfYmVsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWCAAAAAAAAAABAQEBAQEBAZRoB4wCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksIhZSMAUOUdJRSlIwNYm91bmRlZF9hYm92ZZRoECiWCAAAAAAAAAABAQEBAQEBAZRoFEsIhZRoGHSUUpSMBl9zaGFwZZRLCIWUjANsb3eUaBAoliAAAAAAAAAAAAC0wgAAtMIAAKDAAACgwNsPScAAAKDAAAAAgAAAAICUaApLCIWUaBh0lFKUjARoaWdolGgQKJYgAAAAAAAAAAAAtEIAALRCAACgQAAAoEDbD0lAAACgQAAAgD8AAIA/lGgKSwiFlGgYdJRSlIwIbG93X3JlcHKUjFtbLTkwLiAgICAgICAgLTkwLiAgICAgICAgIC01LiAgICAgICAgIC01LiAgICAgICAgIC0zLjE0MTU5MjcgIC01LgogIC0wLiAgICAgICAgIC0wLiAgICAgICBdlIwJaGlnaF9yZXBylIxTWzkwLiAgICAgICAgOTAuICAgICAgICAgNS4gICAgICAgICA1LiAgICAgICAgIDMuMTQxNTkyNyAgNS4KICAxLiAgICAgICAgIDEuICAgICAgIF2UjApfbnBfcmFuZG9tlE51Yi4=",
+        "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:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=",
+        "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:c1b27e99b1aa21925315c7eb9101400c61fcc6f352ec06c7adf33002425cb065
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:21d707c66e1a6862e4f34c92c35bd779abc4ad7e330c3b806c9ca205c1828dd9
+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-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.1.0+cu118
+- GPU Enabled: True
+- Numpy: 1.23.5
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

@@ -0,0 +1 @@
+{"mean_reward": 254.19609680000002, "std_reward": 17.547288960684195, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-12-08T11:09:14.020903"}