Upload ppo-LunarLander-v2 model

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: 256.90 +/- 21.71
+      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 0x7f84b4601990>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f84b4601a20>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f84b4601ab0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f84b4601b40>", "_build": "<function ActorCriticPolicy._build at 0x7f84b4601bd0>", "forward": "<function ActorCriticPolicy.forward at 0x7f84b4601c60>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f84b4601cf0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f84b4601d80>", "_predict": "<function ActorCriticPolicy._predict at 0x7f84b4601e10>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f84b4601ea0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f84b4601f30>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f84b4601fc0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f84b45fa980>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 49152, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1685915101759648738, "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": 0.950848, "_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": 256, "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-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023", "Python": "3.10.11", "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:dbb1e7575b945d596338ac50fb4fc66b1f32a5be55511b46c1c0781231529d50
+size 146740

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 0x7f84b4601990>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f84b4601a20>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f84b4601ab0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f84b4601b40>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f84b4601bd0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f84b4601c60>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f84b4601cf0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f84b4601d80>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f84b4601e10>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f84b4601ea0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f84b4601f30>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f84b4601fc0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f84b45fa980>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 49152,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1685915101759648738,
+    "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": 0.950848,
+    "_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": 256,
+    "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 sha256:7aecf1288eb83e930d19bdbfe9114fef24dc0cb28e2a629c5cb03c40461d0a5e
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a260af6e263cec60d6d33b0db80cf6f2a6e390f6d6659692499096fa970a238d
+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-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023
+- Python: 3.10.11
+- 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": 256.8981237, "std_reward": 21.708527159067888, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-06-04T21:58:25.779639"}