MikhailKon
commited on
Commit
•
27b02a5
1
Parent(s):
99b438a
Upload PPO LunarLander-v2 trained agent
Browse files- README.md +1 -1
- Test_2.zip +3 -0
- Test_2/_stable_baselines3_version +1 -0
- Test_2/data +91 -0
- Test_2/policy.optimizer.pth +3 -0
- Test_2/policy.pth +3 -0
- Test_2/pytorch_variables.pth +3 -0
- Test_2/system_info.txt +7 -0
- config.json +1 -1
- 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: 262.31 +/- 16.12
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name: mean_reward
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task:
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type: reinforcement-learning
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Test_2.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:ee165d0036637b926e068b0acc632924148057ea1e4d7790ae0fa700aed49e71
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size 143145
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Test_2/_stable_baselines3_version
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1.5.0
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Test_2/data
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{
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"policy_class": {
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":type:": "<class 'abc.ABCMeta'>",
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":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
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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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"__init__": "<function ActorCriticPolicy.__init__ at 0x7f82313f0830>",
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"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f82313f08c0>",
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"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f82313f0950>",
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"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f82313f09e0>",
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"_build": "<function ActorCriticPolicy._build at 0x7f82313f0a70>",
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"forward": "<function ActorCriticPolicy.forward at 0x7f82313f0b00>",
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"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f82313f0b90>",
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"_predict": "<function ActorCriticPolicy._predict at 0x7f82313f0c20>",
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"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f82313f0cb0>",
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"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f82313f0d40>",
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"predict_values": "<function ActorCriticPolicy.predict_values at 0x7f82313f0dd0>",
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"__abstractmethods__": "frozenset()",
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"_abc_impl": "<_abc_data object at 0x7f8231433c00>"
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},
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"verbose": 1,
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"policy_kwargs": {},
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"observation_space": {
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":type:": "<class 'gym.spaces.box.Box'>",
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"dtype": "float32",
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"_shape": [
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8
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],
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"low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
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"high": "[inf inf inf inf inf inf inf inf]",
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"bounded_below": "[False False False False False False False False]",
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"bounded_above": "[False False False False False False False False]",
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"_np_random": null
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},
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"action_space": {
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":type:": "<class 'gym.spaces.discrete.Discrete'>",
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"n": 4,
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"_shape": [],
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"dtype": "int64",
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"_np_random": null
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},
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"n_envs": 16,
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"num_timesteps": 524288,
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"_total_timesteps": 500000,
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"_num_timesteps_at_start": 0,
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"seed": null,
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"action_noise": null,
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"start_time": 1652362115.106346,
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"learning_rate": 0.001,
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"tensorboard_log": null,
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"lr_schedule": {
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":type:": "<class 'function'>",
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},
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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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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. 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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. 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