YanMaksi commited on
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1 Parent(s): 86f2fbf

Upload PPO LunarLander-v2 trained agent

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README.md CHANGED
@@ -16,7 +16,7 @@ model-index:
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  type: LunarLander-v2
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  metrics:
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  - type: mean_reward
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- value: 254.40 +/- 23.34
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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: -53.80 +/- 141.80
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  name: mean_reward
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  verified: false
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  ---
config.json CHANGED
@@ -1 +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 0x7fe00ddc40d0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fe00ddc4160>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fe00ddc41f0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fe00ddc4280>", "_build": "<function 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- "gamma": 0.999,
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- "gae_lambda": 0.99,
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  "vf_coef": 0.7,
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  "max_grad_norm": 9,
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  },
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  "normalize_advantage": true,
 
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  ":type:": "<class 'abc.ABCMeta'>",
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  "__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 0x7fbb29f5a040>",
8
+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fbb29f5a0d0>",
9
+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fbb29f5a160>",
10
+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fbb29f5a1f0>",
11
+ "_build": "<function ActorCriticPolicy._build at 0x7fbb29f5a280>",
12
+ "forward": "<function ActorCriticPolicy.forward at 0x7fbb29f5a310>",
13
+ "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fbb29f5a3a0>",
14
+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fbb29f5a430>",
15
+ "_predict": "<function ActorCriticPolicy._predict at 0x7fbb29f5a4c0>",
16
+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fbb29f5a550>",
17
+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fbb29f5a5e0>",
18
+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fbb29f5a670>",
19
  "__abstractmethods__": "frozenset()",
20
+ "_abc_impl": "<_abc_data object at 0x7fbb29f4cf90>"
21
  },
22
  "verbose": 1,
23
  "policy_kwargs": {},
24
  "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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  ],
31
  "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
32
  "high": "[inf inf inf inf inf inf inf inf]",
33
+ "bounded_below": "[False False False False False False False False]",
34
+ "bounded_above": "[False False False False False False False False]",
35
  "_np_random": null
36
  },
37
  "action_space": {
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