Upload PPO LunarLander-v2 trained 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: 278.85 +/- 13.55
+      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 0x7d18515dd7e0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7d18515dd870>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7d18515dd900>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7d18515dd990>", "_build": "<function ActorCriticPolicy._build at 0x7d18515dda20>", "forward": "<function ActorCriticPolicy.forward at 0x7d18515ddab0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7d18515ddb40>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7d18515ddbd0>", "_predict": "<function ActorCriticPolicy._predict at 0x7d18515ddc60>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7d18515ddcf0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7d18515ddd80>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7d18515dde10>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7d18515d23c0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1690364486529099456, "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": 496, "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": 8, "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-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 sha256:17f70e940acecbed633f4cc8b723e89a46e0ca7af88127011ee2438e2c91c791
+size 146649

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 0x7d18515dd7e0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7d18515dd870>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7d18515dd900>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7d18515dd990>",
+        "_build": "<function ActorCriticPolicy._build at 0x7d18515dda20>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7d18515ddab0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7d18515ddb40>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7d18515ddbd0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7d18515ddc60>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7d18515ddcf0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7d18515ddd80>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7d18515dde10>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7d18515d23c0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1690364486529099456,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAGaJirzPn3u8xRBOvgoqqjxeVtY9Ql+IvQAAgD8AAIA/Zk7au1wLWLpO6Jm7LBELOXthjrtiKiU6AACAPwAAgD/NpKm7vWiWPq93gr3LnMS+8zwvvFpQ+b0AAAAAAAAAADP0zjykakC7dzcLO7VrhDwSxYi86uNkPQAAgD8AAIA/WlmFvUgVn7panCSzsqo5Kn+E9rpIlcUzAACAPwAAgD/NiKq7pIJVu3PYZ75OlEE9ltgCPVqJzL0AAIA/AACAP7OJyb2Fdeq7kv6sPLncpDx6BTo9lc0UuwAAgD8AAAAAmrYTvQpD8j4S0Ao98ejdvhEn0jsHC6U9AAAAAAAAAAAm+k4+8ymHP2QDzD454S2/HNiRPtqymD0AAAAAAAAAAFPAnb52oGk/DkRcvhyACb8NlfK+hsffvQAAAAAAAAAAup+/Ppf8i732/Hw8bf6Mu8FaTb762+c8AACAPwAAAADNrlM8SAuCumN067rxqta1qrqGuu13CToAAIA/AACAP2ZIV7w0qMs+S7JzOckDzL5YDoQ8RexrPAAAAAAAAAAAmnXLvNyysj+GOvO+yyc9viQ22Dur7Ye9AAAAAAAAAADmWS89YxKDP1ak4T1ihRi/aTEhPX6npj0AAAAAAAAAAGbkvrwUEL66POc5PM/Wkzz0aa27KH1/PQAAgD8AAIA/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksQSwiGlIwBQ5R0lFKULg=="
+    },
+    "_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": 496,
+    "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": 8,
+    "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 sha256:5f0180310f1ae5a1880cf4af062036974ca51700c632e608540ac2596a463087
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5786ab407555f8eda76c9683c9021d646b382ff42ac2f358769d378ebaa54fef
+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": 278.8517855, "std_reward": 13.553847799000463, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-07-26T10:05:27.804698"}