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: 250.51 +/- 32.56
+      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 0x7d7746cb4820>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7d7746cb48b0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7d7746cb4940>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7d7746cb49d0>", "_build": "<function ActorCriticPolicy._build at 0x7d7746cb4a60>", "forward": "<function ActorCriticPolicy.forward at 0x7d7746cb4af0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7d7746cb4b80>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7d7746cb4c10>", "_predict": "<function ActorCriticPolicy._predict at 0x7d7746cb4ca0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7d7746cb4d30>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7d7746cb4dc0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7d7746cb4e50>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7d7746ca9b80>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1692150794327244018, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAJo1Vjzh4LK6Eu4WOG3MBDPIuSQ6rXsstwAAgD8AAIA/YPBwvvlgMz8WNT8+x2p2vkTLi71j8/s8AAAAAAAAAACGl2E+DQ4KvaRltrrHUWg5V6p4vrMW/TkAAIA/AACAPzOE+TygH+g+82rsPYWIor5duT89Ef4NvQAAAAAAAAAAjUisPY+uNLpwetewvBFhMa1NlDp9T9GyAACAPwAAAACm9Ym9f/ALP8U+fD0MpW6+xzztu0VjkDwAAAAAAAAAAM2lij24RK275/I1vRLQP76ZfZi8ytaDPgAAgD8AAAAABuoTPuz757s4WXE6atGpuGrVRr1o4JG5AACAPwAAgD+aEiG+AuGaP+CnFr0uE4a+Ou8qvk3U8D0AAAAAAAAAAIBzFz2u7ZW64u0tMsCbejDWjGW67ptpsgAAgD8AAIA/szRQPVOqaT+usZA9ylacvnlizzuQ+E89AAAAAAAAAABmhT8+J81sPz5Fkj00UJK+/SAXPr+lF74AAAAAAAAAAJox0zy4j7o8PIWWvfJZM77ot0S9HHsTPQAAAAAAAAAATW8QvVQ4hD/NNYY8M5PGvpPn1bw7Sq28AAAAAAAAAADNUdk8fsGiPoMhZ76g0jS+Mou2vQYxor0AAAAAAAAAAM3boz2UZfs90va5vKb7S74OaRM9k0BiPQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////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": 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 sha256:54eb0584a853bd1c0726a8978c7184d39c6f710ee4f5143988bf0a4d41001cbb
+size 146742

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 0x7d7746cb4820>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7d7746cb48b0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7d7746cb4940>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7d7746cb49d0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7d7746cb4a60>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7d7746cb4af0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7d7746cb4b80>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7d7746cb4c10>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7d7746cb4ca0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7d7746cb4d30>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7d7746cb4dc0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7d7746cb4e50>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7d7746ca9b80>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1692150794327244018,
+    "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:ed9047c5f56ae2036fe8c2af51386b68643445bacfa01ee44d065eed9ce54394
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bc59978696265fba9860a461c6f2167e9c5893a8c1a6e31dd856f9448ab1c645
+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": 250.5132353, "std_reward": 32.55716868289477, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-08-16T02:23:00.357533"}