Upload PPO LunarLander-v2 trained agent

README.md

@@ -1,3 +1,37 @@
 ---
-license: openrail
+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: 256.80 +/- 22.87
+      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 0x7f4344604160>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f43446041f0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f4344604280>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f4344604310>", "_build": "<function ActorCriticPolicy._build at 0x7f43446043a0>", "forward": "<function ActorCriticPolicy.forward at 0x7f4344604430>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f43446044c0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f4344604550>", "_predict": "<function ActorCriticPolicy._predict at 0x7f43446045e0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f4344604670>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f4344604700>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f4344604790>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f43445f1b80>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1688085376538315351, "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:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuEQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz8zqSowVTJhhZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "system_info": {"OS": "Linux-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 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-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c225a16b549f8dba19feda4bf2617b4facd438f0f4e6b581069e5022097f7998
+size 146739

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 0x7f4344604160>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f43446041f0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f4344604280>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f4344604310>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f43446043a0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f4344604430>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f43446044c0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f4344604550>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f43446045e0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f4344604670>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f4344604700>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f4344604790>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f43445f1b80>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1688085376538315351,
+    "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:e1820c2c2b509e694ec07b79a748a86679127ed6d448d07c5b6821d16969302f
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fe703cee145feaa6749da4e5bd53ef380f5bdcff5c8d2e3ec3dab7e18c6a4fa6
+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-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 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": 256.79713598026365, "std_reward": 22.868810152343116, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-06-30T01:08:35.560517"}