First 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: -69.69 +/- 27.21
+      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 0x7efa24e77ac0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7efa24e77b50>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7efa24e77be0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7efa24e77c70>", "_build": "<function ActorCriticPolicy._build at 0x7efa24e77d00>", "forward": "<function ActorCriticPolicy.forward at 0x7efa24e77d90>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7efa24e77e20>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7efa24e77eb0>", "_predict": "<function ActorCriticPolicy._predict at 0x7efa24e77f40>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7efa24e7c040>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7efa24e7c0d0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7efa24e7c160>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7efa24e0d1c0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 131072, "_total_timesteps": 100000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1710904310402944965, "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": -0.3107200000000001, "_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": 75, "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": 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-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.2.1+cu121", "GPU Enabled": "True", "Numpy": "1.25.2", "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:fe9e6d8bd396052105420acf1d810065fd849791b1036de3cd439082c39b39a3
+size 148026

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 0x7efa24e77ac0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7efa24e77b50>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7efa24e77be0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7efa24e77c70>",
+        "_build": "<function ActorCriticPolicy._build at 0x7efa24e77d00>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7efa24e77d90>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7efa24e77e20>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7efa24e77eb0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7efa24e77f40>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7efa24e7c040>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7efa24e7c0d0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7efa24e7c160>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7efa24e0d1c0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 131072,
+    "_total_timesteps": 100000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1710904310402944965,
+    "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": -0.3107200000000001,
+    "_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": 75,
+    "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": 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:e54a02d472079f4e2ce2d48d0270b1aac5389dfb85c0a84831fe54ba0b5f713f
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bdd4084b54329e82f8373f5078938a4e164f965127d4e0bb9e26ceb6519f62af
+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-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.2.1+cu121
+- GPU Enabled: True
+- Numpy: 1.25.2
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

@@ -0,0 +1 @@
+{"mean_reward": -69.6894728, "std_reward": 27.213235832853268, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-03-20T03:18:14.286293"}