Add trained PPO agent

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: 288.73 +/- 10.81
+      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 0x7e4e5bac3eb0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7e4e5bac3f40>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7e4e5bac8040>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7e4e5bac80d0>", "_build": "<function ActorCriticPolicy._build at 0x7e4e5bac8160>", "forward": "<function ActorCriticPolicy.forward at 0x7e4e5bac81f0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7e4e5bac8280>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7e4e5bac8310>", "_predict": "<function ActorCriticPolicy._predict at 0x7e4e5bac83a0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7e4e5bac8430>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7e4e5bac84c0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7e4e5bac8550>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7e4e5bac4500>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1689711824629863753, "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": 310, "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": 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:": "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-5.15.109+-x86_64-with-glibc2.31 # 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": "1.22.4", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}

ppo-lunarlander.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4e20144c7692c3ba621803129dd7eba2aa7083b33e4c6454af15430634f5cf78
+size 146637

ppo-lunarlander/_stable_baselines3_version

@@ -0,0 +1 @@
+2.0.0a5

ppo-lunarlander/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 0x7e4e5bac3eb0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7e4e5bac3f40>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7e4e5bac8040>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7e4e5bac80d0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7e4e5bac8160>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7e4e5bac81f0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7e4e5bac8280>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7e4e5bac8310>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7e4e5bac83a0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7e4e5bac8430>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7e4e5bac84c0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7e4e5bac8550>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7e4e5bac4500>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1689711824629863753,
+    "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": 310,
+    "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": 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/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ae36025f3001764519b8197815b3206c3e04cce406e8c891ac948dd61fed3813
+size 87929

ppo-lunarlander/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b5548c2b83807e74c766658aa0bfbcd8a9533740ae5ab72ebaeabc8cab733d00
+size 43329

ppo-lunarlander/pytorch_variables.pth

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

ppo-lunarlander/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-5.15.109+-x86_64-with-glibc2.31 # 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: 1.22.4
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

@@ -0,0 +1 @@
+{"mean_reward": 288.7269635, "std_reward": 10.806034695872963, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-07-18T20:50:32.861151"}