training a model on local pc
Browse files- README.md +1 -1
- config.json +1 -1
- ppo-LunarLander-v2.zip +2 -2
- ppo-LunarLander-v2/data +34 -31
- ppo-LunarLander-v2/policy.optimizer.pth +2 -2
- ppo-LunarLander-v2/policy.pth +1 -1
- ppo-LunarLander-v2/system_info.txt +3 -3
- replay.mp4 +0 -0
- results.json +1 -1
README.md
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type: LunarLander-v2
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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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verified: false
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---
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type: LunarLander-v2
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metrics:
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- type: mean_reward
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value: 281.10 +/- 23.96
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name: mean_reward
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verified: false
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---
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config.json
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-
{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__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 ", "__init__": "<function ActorCriticPolicy.__init__ at 0x7fc031974040>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fc0319740d0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fc031974160>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fc0319741f0>", "_build": "<function ActorCriticPolicy._build at 0x7fc031974280>", "forward": "<function ActorCriticPolicy.forward at 0x7fc031974310>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fc0319743a0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fc031974430>", "_predict": "<function ActorCriticPolicy._predict at 0x7fc0319744c0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fc031974550>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fc0319745e0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fc031974670>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fc03196f570>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": 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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 0x7f6764bf1870>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f6764bf1900>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f6764bf1990>", 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| 82 |
"gae_lambda": 0.98,
|
|
@@ -87,7 +90,7 @@
|
|
| 87 |
"n_epochs": 4,
|
| 88 |
"clip_range": {
|
| 89 |
":type:": "<class 'function'>",
|
| 90 |
-
":serialized:": "
|
| 91 |
},
|
| 92 |
"clip_range_vf": null,
|
| 93 |
"normalize_advantage": true,
|
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|
| 4 |
":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
|
| 5 |
"__module__": "stable_baselines3.common.policies",
|
| 6 |
"__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 ",
|
| 7 |
+
"__init__": "<function ActorCriticPolicy.__init__ at 0x7f6764bf1870>",
|
| 8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f6764bf1900>",
|
| 9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f6764bf1990>",
|
| 10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f6764bf1a20>",
|
| 11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7f6764bf1ab0>",
|
| 12 |
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"forward": "<function ActorCriticPolicy.forward at 0x7f6764bf1b40>",
|
| 13 |
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"extract_features": "<function ActorCriticPolicy.extract_features at 0x7f6764bf1bd0>",
|
| 14 |
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"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f6764bf1c60>",
|
| 15 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7f6764bf1cf0>",
|
| 16 |
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"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f6764bf1d80>",
|
| 17 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f6764bf1e10>",
|
| 18 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7f6764bf1ea0>",
|
| 19 |
"__abstractmethods__": "frozenset()",
|
| 20 |
+
"_abc_impl": "<_abc._abc_data object at 0x7f6764beae40>"
|
| 21 |
},
|
| 22 |
"verbose": 1,
|
| 23 |
"policy_kwargs": {},
|
| 24 |
"observation_space": {
|
| 25 |
":type:": "<class 'gym.spaces.box.Box'>",
|
| 26 |
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|
| 27 |
"dtype": "float32",
|
| 28 |
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"bounded_below": "[ True True True True True True True True]",
|
| 29 |
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"bounded_above": "[ True True True True True True True True]",
|
| 30 |
"_shape": [
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| 31 |
8
|
| 32 |
],
|
| 33 |
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"low": "[-1.5 -1.5 -5. -5. -3.1415927 -5.\n -0. -0. ]",
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| 34 |
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"high": "[1.5 1.5 5. 5. 3.1415927 5. 1.\n 1. ]",
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| 35 |
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"low_repr": "[-1.5 -1.5 -5. -5. -3.1415927 -5.\n -0. -0. ]",
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| 36 |
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"high_repr": "[1.5 1.5 5. 5. 3.1415927 5. 1.\n 1. ]",
|
| 37 |
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"_np_random": null
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| 38 |
},
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| 39 |
"action_space": {
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| 40 |
":type:": "<class 'gym.spaces.discrete.Discrete'>",
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| 41 |
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