first_commit

README.md

@@ -1,3 +1,37 @@
 ---
-license: apache-2.0
+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: 200.53 +/- 92.64
+      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 0x7867b88bfe20>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7867b88bfeb0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7867b88bff40>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7867b88c0040>", "_build": "<function ActorCriticPolicy._build at 0x7867b88c00d0>", "forward": "<function ActorCriticPolicy.forward at 0x7867b88c0160>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7867b88c01f0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7867b88c0280>", "_predict": "<function ActorCriticPolicy._predict at 0x7867b88c0310>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7867b88c03a0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7867b88c0430>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7867b88c04c0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7867b8a60d00>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1000448, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1708376715707395992, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAHOAlD7BChU/qCgTvkAmhL5/Uaw8GiBlvAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.00044800000000000395, "_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": 3908, "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": 1, "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:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuEQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz/JmZmZmZmahZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "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": "2.1.0+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:dc0ff3fb3c2b019ecc3258e7b6fcca6ce12b9fbe64c099671de32253108605ad
+size 147426

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 0x7867b88bfe20>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7867b88bfeb0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7867b88bff40>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7867b88c0040>",
+        "_build": "<function ActorCriticPolicy._build at 0x7867b88c00d0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7867b88c0160>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7867b88c01f0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7867b88c0280>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7867b88c0310>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7867b88c03a0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7867b88c0430>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7867b88c04c0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7867b8a60d00>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1000448,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1708376715707395992,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAHOAlD7BChU/qCgTvkAmhL5/Uaw8GiBlvAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": null,
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": -0.00044800000000000395,
+    "_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": 3908,
+    "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": 1,
+    "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 sha256:3fe290c6981a1c02ed2f99c0b30f1187d24f33d7cf8f2e15f5edcd4d3f49e8b9
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:99c14c275b87149c3c0e52207eb5b20fa63a273d8e69fd571319afa4d4f997ff
+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: 2.1.0+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": 200.5282352, "std_reward": 92.64317743209561, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-02-19T21:41:07.471474"}