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: 243.29 +/- 18.13
+      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 0x79947a8a7520>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x79947a8a75b0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x79947a8a7640>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x79947a8a76d0>", "_build": "<function ActorCriticPolicy._build at 0x79947a8a7760>", "forward": "<function ActorCriticPolicy.forward at 0x79947a8a77f0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x79947a8a7880>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x79947a8a7910>", "_predict": "<function ActorCriticPolicy._predict at 0x79947a8a79a0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x79947a8a7a30>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x79947a8a7ac0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x79947a8a7b50>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x79947a89bfc0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1691991608748058428, "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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"}, "system_info": {"OS": "Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.23.5", "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:aa26575d5df1d0fc33f8acdb6b9694eee4177b1ae4a4b2743351aee75509b516
+size 146750

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 0x79947a8a7520>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x79947a8a75b0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x79947a8a7640>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x79947a8a76d0>",
+        "_build": "<function ActorCriticPolicy._build at 0x79947a8a7760>",
+        "forward": "<function ActorCriticPolicy.forward at 0x79947a8a77f0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x79947a8a7880>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x79947a8a7910>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x79947a8a79a0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x79947a8a7a30>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x79947a8a7ac0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x79947a8a7b50>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x79947a89bfc0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1691991608748058428,
+    "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 sha256:1c2f48b48746910f993407051560b08f4bb8f68029426fa1f98ee8b66cc044d4
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7121f6fc43919128a963f68165a32a294e174b15614468a411034a1f47971f5d
+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: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.0.1+cu118
+- GPU Enabled: True
+- Numpy: 1.23.5
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

@@ -0,0 +1 @@
+{"mean_reward": 243.28686309999998, "std_reward": 18.12977137302999, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-08-14T06:03:03.714254"}