first commit

README.md

@@ -0,0 +1,36 @@
+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - metrics:
+    - type: mean_reward
+      value: -196.21 +/- 152.53
+      name: mean_reward
+    task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+---
+
+# **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

@@ -1 +1 @@
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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 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 0x16a9faee0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x16a9faf70>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x16a9ff040>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor 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ppo-LunarLander-v2.zip

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         "__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 sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\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 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 0x16a9faee0>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x16a9faf70>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x16a9ff040>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x16a9ff0d0>",
-        "_build": "<function ActorCriticPolicy._build at 0x16a9ff160>",
-        "forward": "<function ActorCriticPolicy.forward at 0x16a9ff1f0>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x16a9ff280>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x16a9ff310>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x16a9ff3a0>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x16a9ff430>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x16a9ff4c0>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x1613d2ee0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x1613d2f70>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x1613d7040>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x1613d70d0>",
+        "_build": "<function ActorCriticPolicy._build at 0x1613d7160>",
+        "forward": "<function ActorCriticPolicy.forward at 0x1613d71f0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x1613d7280>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x1613d7310>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x1613d73a0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x1613d7430>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x1613d74c0>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x16a9f1d80>"
+        "_abc_impl": "<_abc_data object at 0x1613cadb0>"
     },
     "verbose": 1,
     "policy_kwargs": {},

ppo-LunarLander-v2/policy.pth

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+oid sha256:b74abae48a6675e8dd1a9737581f3f8e2bc66d9ff284f6e4b1c52fd86548c04b
 size 43073

results.json

@@ -1 +1 @@
-{"mean_reward": -709.5626455118879, "std_reward": 178.98351948538613, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-06-11T00:43:58.421923"}
+{"mean_reward": -196.20948735694475, "std_reward": 152.5321181844002, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-06-11T00:52:27.806874"}