Upload trained agent

README.md

@@ -16,7 +16,7 @@ model-index:
  type: LunarLander-v2
  metrics:
  - type: mean_reward
- value: -74.10 +/- 117.96
+ value: 272.15 +/- 17.31
  name: mean_reward
  verified: false
 ---

config.json

@@ -1 +1 @@
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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 0x7da6a42ab520>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7da6a42ab5b0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7da6a42ab640>", 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"Linux-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.1.0+cu121", "GPU Enabled": "True", "Numpy": "1.23.5", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}
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It allows to keep variance\n above zero and prevent it from growing too fast. 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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 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 ",
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- "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7da6a42ab640>",
- "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7da6a42ab6d0>",
- "_build": "<function ActorCriticPolicy._build at 0x7da6a42ab760>",
- "forward": "<function ActorCriticPolicy.forward at 0x7da6a42ab7f0>",
- "extract_features": "<function ActorCriticPolicy.extract_features at 0x7da6a42ab880>",
- "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7da6a42ab910>",
- "_predict": "<function ActorCriticPolicy._predict at 0x7da6a42ab9a0>",
- "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7da6a42aba30>",
- "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7da6a42abac0>",
- "predict_values": "<function ActorCriticPolicy.predict_values at 0x7da6a42abb50>",
+ "__init__": "<function ActorCriticPolicy.__init__ at 0x784caca600d0>",
+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x784caca60160>",
+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x784caca601f0>",
+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x784caca60280>",
+ "_build": "<function ActorCriticPolicy._build at 0x784caca60310>",
+ "forward": "<function ActorCriticPolicy.forward at 0x784caca603a0>",
+ "extract_features": "<function ActorCriticPolicy.extract_features at 0x784caca60430>",
+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x784caca604c0>",
+ "_predict": "<function ActorCriticPolicy._predict at 0x784caca60550>",
+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x784caca605e0>",
+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x784caca60670>",
+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x784caca60700>",
  "__abstractmethods__": "frozenset()",
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ppo-LunarLander-v2/policy.pth

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results.json

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-{"mean_reward": -74.09572638339304, "std_reward": 117.96315855237414, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-01-16T16:55:38.377365"}
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