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: 265.35 +/- 21.03
+      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 0x7a3380075ab0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7a3380075b40>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7a3380075bd0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7a3380075c60>", "_build": "<function ActorCriticPolicy._build at 0x7a3380075cf0>", "forward": "<function ActorCriticPolicy.forward at 0x7a3380075d80>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7a3380075e10>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7a3380075ea0>", "_predict": "<function ActorCriticPolicy._predict at 0x7a3380075f30>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7a3380075fc0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7a3380076050>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7a33800760e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7a3380213d40>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1694377541056559047, "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 sha256:76bc062f35a563007f862560eb99d3060f08857b911ab960aa7eed226da941fb
+size 146738

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 0x7a3380075ab0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7a3380075b40>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7a3380075bd0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7a3380075c60>",
+        "_build": "<function ActorCriticPolicy._build at 0x7a3380075cf0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7a3380075d80>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7a3380075e10>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7a3380075ea0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7a3380075f30>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7a3380075fc0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7a3380076050>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7a33800760e0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7a3380213d40>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1694377541056559047,
+    "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:a7fb51e11c730781a3bcad2cd2f1807a2ac9c2fe44a1676c5f3203e3840e2a07
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4212dbe299d509bb1da371f1abc2dd31fd45382041963a94f9c98e0b1f744e2f
+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": 265.34730678923677, "std_reward": 21.028653813183002, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-09-10T20:50:08.397250"}