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: 245.73 +/- 31.98
+      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 0x7d9ec13cce50>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7d9ec13ccee0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7d9ec13ccf70>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7d9ec13cd000>", "_build": "<function ActorCriticPolicy._build at 0x7d9ec13cd090>", "forward": "<function ActorCriticPolicy.forward at 0x7d9ec13cd120>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7d9ec13cd1b0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7d9ec13cd240>", "_predict": "<function ActorCriticPolicy._predict at 0x7d9ec13cd2d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7d9ec13cd360>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7d9ec13cd3f0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7d9ec13cd480>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7d9ec136db00>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1697927252023142068, "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:a5cbacc582d731124ba6e08e9bd63dc17981d0ed0fdb332e6352eee261ebabb9
+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 0x7d9ec13cce50>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7d9ec13ccee0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7d9ec13ccf70>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7d9ec13cd000>",
+        "_build": "<function ActorCriticPolicy._build at 0x7d9ec13cd090>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7d9ec13cd120>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7d9ec13cd1b0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7d9ec13cd240>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7d9ec13cd2d0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7d9ec13cd360>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7d9ec13cd3f0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7d9ec13cd480>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7d9ec136db00>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1697927252023142068,
+    "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 sha256:388871052d5d7521d22f0cd3c3b459bf2f3ca49e0928ab026a4856790ed98435
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eb0fffacbe02c009250eb791286c9c7bdfc028784f3e3d6a301d3824a2192fe1
+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": 245.73134610000002, "std_reward": 31.980334647551, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-10-21T22:52:18.549063"}