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Browse files
README.md CHANGED
@@ -16,7 +16,7 @@ model-index:
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  type: PandaReachDense-v2
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  metrics:
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  },
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- "n_steps": 5,
 
 
 
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  "gamma": 0.99,
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- "vf_coef": 0.5,
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- "max_grad_norm": 0.5,
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- "normalize_advantage": false
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  }
 
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  {
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  "policy_class": {
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  ":type:": "<class 'abc.ABCMeta'>",
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+ "__doc__": "\n Policy class (with both actor and critic) for TQC.\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 use_sde: Whether to use State Dependent Exploration or not\n :param log_std_init: Initial value for the log standard deviation\n :param use_expln: Use ``expln()`` function instead of ``exp()`` when using gSDE 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 clip_mean: Clip the mean output when using gSDE to avoid numerical instability.\n :param features_extractor_class: Features extractor to use.\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 :param n_quantiles: Number of quantiles for the critic.\n :param n_critics: Number of critic networks to create.\n :param share_features_extractor: Whether to share or not the features extractor\n between the actor and the critic (this saves computation time)\n ",
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+ "__init__": "<function MultiInputPolicy.__init__ at 0x7f2930f5a160>",
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  "__abstractmethods__": "frozenset()",
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+ "_abc_impl": "<_abc_data object at 0x7f2930fd5390>"
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  },
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  "verbose": 1,
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  "policy_kwargs": {
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+ "use_sde": false
 
 
 
 
 
 
 
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  },
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  "observation_space": {
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  ":type:": "<class 'gym.spaces.dict.Dict'>",
 
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  },
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  "action_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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  3
 
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  "high": "[1. 1. 1.]",
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  "bounded_below": "[ True True True]",
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  "bounded_above": "[ True True True]",
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+ "_np_random": "RandomState(MT19937)"
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  },
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  "n_envs": 4,
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  "num_timesteps": 1000000,
 
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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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  "tensorboard_log": null,
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  "lr_schedule": {
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