Upload PPO LunarLander-v2 trained agent, possibly

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: 242.52 +/- 69.83
+      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 0x7ff311830c10>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ff311830ca0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ff311830d30>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ff311830dc0>", "_build": "<function ActorCriticPolicy._build at 0x7ff311830e50>", "forward": "<function ActorCriticPolicy.forward at 0x7ff311830ee0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7ff311830f70>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ff311831000>", "_predict": "<function ActorCriticPolicy._predict at 0x7ff311831090>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ff311831120>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ff3118311b0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ff311831240>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7ff311826080>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1690813749434435667, "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-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023", "Python": "3.10.6", "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:ce1671eeca6e52c5c04f7cae10aa1404d7e139a5db95762b80b770af6ad6bb48
+size 146733

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 0x7ff311830c10>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ff311830ca0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ff311830d30>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ff311830dc0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7ff311830e50>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7ff311830ee0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7ff311830f70>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ff311831000>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7ff311831090>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ff311831120>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ff3118311b0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ff311831240>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7ff311826080>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1690813749434435667,
+    "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"
+    }
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:65354e39e97ff97216c6eb3f8343c9ad13cd71754ccfd6ab893f46a5e3010a18
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5d4af907f20bdb2063c449dd039cdee2c341b9d7b158552f0d0b641999de91dc
+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-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023
+- Python: 3.10.6
+- 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": 242.51724038344383, "std_reward": 69.82503061897906, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-07-31T15:33:07.904675"}