demo colab training agent ppo-LunarLander-v2, longer training
Browse files- README.md +1 -1
- config.json +1 -1
- ppo-LunarLander-v2-2M.zip +3 -0
- ppo-LunarLander-v2-2M/_stable_baselines3_version +1 -0
- ppo-LunarLander-v2-2M/data +94 -0
- ppo-LunarLander-v2-2M/policy.optimizer.pth +3 -0
- ppo-LunarLander-v2-2M/policy.pth +3 -0
- ppo-LunarLander-v2-2M/pytorch_variables.pth +3 -0
- ppo-LunarLander-v2-2M/system_info.txt +7 -0
- replay.mp4 +2 -2
- results.json +1 -1
README.md
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results:
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- metrics:
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- type: mean_reward
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value:
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name: mean_reward
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task:
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type: reinforcement-learning
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results:
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- metrics:
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- type: mean_reward
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value: 277.09 +/- 18.99
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name: mean_reward
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task:
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type: reinforcement-learning
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config.json
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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 0x7fb0ea24d710>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fb0ea24d7a0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fb0ea24d830>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fb0ea24d8c0>", "_build": "<function 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"__module__": "stable_baselines3.common.policies",
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"__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 ",
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"target_kl": null
|
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ppo-LunarLander-v2-2M/policy.optimizer.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:e7341030c69fd34fe0eecf3944c5bc9a9a791f913e41e7e85bda36c98881ee1c
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size 84893
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ppo-LunarLander-v2-2M/policy.pth
ADDED
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version https://git-lfs.github.com/spec/v1
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oid sha256:78f9aa8c2ddf3262aa24487590121f2e0e367c4af338b83efcc903dd9fbf09f6
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size 43201
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ppo-LunarLander-v2-2M/pytorch_variables.pth
ADDED
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ppo-LunarLander-v2-2M/system_info.txt
ADDED
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OS: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022
|
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Python: 3.7.13
|
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Stable-Baselines3: 1.5.0
|
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PyTorch: 1.11.0+cu113
|
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GPU Enabled: True
|
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Numpy: 1.21.6
|
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Gym: 0.21.0
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replay.mp4
CHANGED
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results.json
CHANGED
@@ -1 +1 @@
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|
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{"mean_reward":
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|
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{"mean_reward": 277.0913240014541, "std_reward": 18.98661539538064, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-06T09:45:56.874332"}
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