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: MLPPO
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+    metrics:
+    - type: mean_reward
+      value: 269.67 +/- 20.04
+      name: mean_reward
+      verified: false
+---
+
+# **MLPPO** Agent playing **LunarLander-v2**
+This is a trained model of a **MLPPO** 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 0x7f070c385630>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f070c3856c0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f070c385750>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f070c3857e0>", "_build": "<function ActorCriticPolicy._build at 0x7f070c385870>", "forward": "<function ActorCriticPolicy.forward at 0x7f070c385900>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f070c385990>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f070c385a20>", "_predict": "<function ActorCriticPolicy._predict at 0x7f070c385ab0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f070c385b40>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f070c385bd0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f070c385c60>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f070c388800>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1693070617234561865, "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-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.0.1+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 sha256:06b1622876a3bbf9a277d03fd124d22cff592485a1b6c1f1b9e359091f877b59
+size 146754

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 0x7f070c385630>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f070c3856c0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f070c385750>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f070c3857e0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f070c385870>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f070c385900>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f070c385990>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f070c385a20>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f070c385ab0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f070c385b40>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f070c385bd0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f070c385c60>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f070c388800>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1693070617234561865,
+    "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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"
+    }
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:37cdf1a7e61c18d8b8e57def9b9a1e305e832479d12894ad05b502d729b56919
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:584d490ab66157bf71dbe7e63f86af7c71c9f96903c832d8d6b970c4cbdd0bbc
+size 43329

ppo-LunarLander-v2/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
+size 431

ppo-LunarLander-v2/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.0.1+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": 269.66814020000004, "std_reward": 20.03881293095719, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-08-26T17:55:21.932508"}