Upload PPO LunarLander-v2 trained agent

README.md

@@ -8,16 +8,17 @@ tags:
 model-index:
 - name: PPO
   results:
-  - metrics:
-    - type: mean_reward
-      value: 224.72 +/- 37.97
-      name: mean_reward
-    task:
+  - task:
       type: reinforcement-learning
       name: reinforcement-learning
     dataset:
       name: LunarLander-v2
       type: LunarLander-v2
+    metrics:
+    - type: mean_reward
+      value: 211.48 +/- 46.96
+      name: mean_reward
+      verified: false
 ---
 
 # **PPO** Agent playing **LunarLander-v2**

config.json

@@ -1 +1 @@
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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 0x7fe8db02da20>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fe8db02dab0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fe8db02db40>", "_build_mlp_extractor": "<function 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ppo-LunarLander-v2.zip

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+oid sha256:bebc3ec89a5afa6225efdc91261ec90cf1f0bf0a72ce5584a9c8abb736f58476
+size 147524

ppo-LunarLander-v2/_stable_baselines3_version

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+1.7.0

ppo-LunarLander-v2/data

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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 0x7fe8db02da20>",
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-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fe8db02df30>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fe8db02dfc0>",
+        "__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 0x7f99c5ecb1c0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f99c5ecb250>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f99c5ecb2e0>",
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+        "_build": "<function ActorCriticPolicy._build at 0x7f99c5ecb400>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f99c5ecb490>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f99c5ecb520>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f99c5ecb5b0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f99c5ecb640>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f99c5ecb6d0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f99c5ecb760>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f99c5ecb7f0>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x7fe8db0346c0>"
+        "_abc_impl": "<_abc._abc_data object at 0x7f99c5ed8e80>"
     },
     "verbose": 1,
     "policy_kwargs": {},
@@ -42,21 +43,21 @@
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ppo-LunarLander-v2/system_info.txt

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-OS: Linux-5.4.0-124-generic-x86_64-with-glibc2.31 #140-Ubuntu SMP Thu Aug 4 02:23:37 UTC 2022
-Python: 3.10.4
-Stable-Baselines3: 1.6.1
-PyTorch: 1.12.1
-GPU Enabled: True
-Numpy: 1.23.1
-Gym: 0.21.0
+- OS: Linux-5.15.0-67-generic-x86_64-with-glibc2.35 # 74-Ubuntu SMP Wed Feb 22 14:14:39 UTC 2023
+- Python: 3.10.6
+- Stable-Baselines3: 1.7.0
+- PyTorch: 2.0.0+cu117
+- GPU Enabled: True
+- Numpy: 1.24.2
+- Gym: 0.21.0

results.json

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