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: 231.72 +/- 34.59
+      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 0x7af460413490>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7af460413520>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7af4604135b0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7af460413640>", "_build": "<function ActorCriticPolicy._build at 0x7af4604136d0>", "forward": "<function ActorCriticPolicy.forward at 0x7af460413760>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7af4604137f0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7af460413880>", "_predict": "<function ActorCriticPolicy._predict at 0x7af460413910>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7af4604139a0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7af460413a30>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7af460413ac0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7af4603bf100>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1695495937216015178, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAOZWY77Mglc/ckd/u1XugL4r8QK+O0fXPQAAAAAAAAAA7YVVvhLF6TwPEzM6OeIBuU6Og75mLIa5AACAPwAAgD+zc869wwUeum2xSzm88ms0Xk8pO1albbgAAIA/AACAP5pJi7pxXBA8iSqivR6TU75jBz88UIYnvQAAAAAAAAAARn4svnGpETwZvz61Jrw7M6WVn70od5c0AACAPwAAgD/AYTW+bkWovJ1b47vHloa6t54hPu0zWDsAAIA/AACAP81bkrx7ppa6pXTGNmC0qzF9cpo68qTptQAAgD8AAIA/TVW2va5fgbpKdAq5hBX8s5cSCDu3cSE4AAAAAAAAgD8A7KW7zge2P8JNA78Us/4+rHzAOy3w7T0AAAAAAAAAAJM2LD6cgJw/37KEvE5ra74i9Xw9XrYWvgAAAAAAAAAAwI6/vY+KLrp9j5O77/yQtQ4SFbuyWK86AAAAAAAAgD9mqGq8KYwVuto82rZTZNWxtYgYO3YQAjYAAIA/AACAPzOubz0UVJi6qhQKuF7k7rKrBpc6jIcfNwAAgD8AAIA/ph1bvi8Nqz4WwOI9S0Y/vm0b4zzbzyq9AAAAAAAAAACm94a9FDyDujNKdLjc/+qzl/TZOqrXizcAAIA/AACAP010471cJ0u6qrDMOq7wSjZKppo7AIjsuQAAgD8AAIA/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksQSwiGlIwBQ5R0lFKULg=="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAQAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 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-mlp.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c6ba593c037ea7d8bf8bd107a9d6229e0cdfd82566fb3da8aba4a26b8a0ffe7f
+size 146759

ppo-LunarLander-v2-mlp/_stable_baselines3_version

@@ -0,0 +1 @@
+2.0.0a5

ppo-LunarLander-v2-mlp/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 0x7af460413490>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7af460413520>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7af4604135b0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7af460413640>",
+        "_build": "<function ActorCriticPolicy._build at 0x7af4604136d0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7af460413760>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7af4604137f0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7af460413880>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7af460413910>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7af4604139a0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7af460413a30>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7af460413ac0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7af4603bf100>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1695495937216015178,
+    "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:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAQAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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-mlp/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:188733d5366084a6efd6e3e9a3befea2951a1cffda19b43381ce615f057ee8ef
+size 87929

ppo-LunarLander-v2-mlp/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:12a2ffce42aa1f3965076dc2497992d8a578a3f25d0732a6805663dbf6e2ddcc
+size 43329

ppo-LunarLander-v2-mlp/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
+size 431

ppo-LunarLander-v2-mlp/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 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": 231.7185250869681, "std_reward": 34.59335223304422, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-09-23T20:18:24.007074"}