class_docstr: Implements a continuous MLP Q-value network.
  
  It predicts the Q-value for all actions based on the input state. It uses
  a PyTorch neural network module to fit the function of Q(s, a).
class_docstr_tok: ["Implements","a","continuous","MLP","Q-value","network",".","It","predicts","the","Q-value","for","all","actions","based","on","the","input","state",".","It","uses","a","PyTorch","neural","network","module","to","fit","the","function","of","Q","(","s",",","a",")","."]
docstr: Initialize class with multiple attributes.
  
  Args:
      env_spec (EnvSpec): Environment specification.
      **kwargs: Keyword arguments.
docstr_tok: ["Initialize","class","with","multiple","attributes",".","Args",":","env_spec","(","EnvSpec",")",":","Environment","specification",".","*","*","kwargs",":","Keyword","arguments","."]
code: def __init__(self, env_spec, **kwargs):
      """Initialize class with multiple attributes.
  
      Args:
          env_spec (EnvSpec): Environment specification.
          **kwargs: Keyword arguments.
  
      """
      self._env_spec = env_spec
      self._obs_dim = env_spec.observation_space.flat_dim
      self._action_dim = env_spec.action_space.flat_dim
  
      MLPModule.__init__(
          self,
          input_dim=self._obs_dim + self._action_dim,
          output_dim=1,
          **kwargs,
      )
  
imports: import torch
  from garage.torch.modules import MLPModule
label_desc: Use image node_id 1 to create a new ContinuousMLPQFunction object from inherited base classes: MLPModule with example: obj = ContinuousMLPQFunction(env_spec)