class_docstr: Implements a deterministic policy network.
  
  The policy network selects action based on the state of the environment.
  It uses a PyTorch neural network module to fit the function of pi(s).
class_docstr_tok: ["Implements","a","deterministic","policy","network",".","The","policy","network","selects","action","based","on","the","state","of","the","environment",".","It","uses","a","PyTorch","neural","network","module","to","fit","the","function","of","pi","(","s",")","."]
docstr: Initialize class with multiple attributes.
  
  Args:
      env_spec (EnvSpec): Environment specification.
      name (str): Policy name.
      **kwargs: Additional keyword arguments passed to the MLPModule.
docstr_tok: ["Initialize","class","with","multiple","attributes",".","Args",":","env_spec","(","EnvSpec",")",":","Environment","specification",".","name","(","str",")",":","Policy","name",".","*","*","kwargs",":","Additional","keyword","arguments","passed","to","the","MLPModule","."]
code: def __init__(self, env_spec, name="DeterministicMLPPolicy", **kwargs):
      """Initialize class with multiple attributes.
  
      Args:
          env_spec (EnvSpec): Environment specification.
          name (str): Policy name.
          **kwargs: Additional keyword arguments passed to the MLPModule.
      """
      super().__init__(env_spec, name)
  
      self._obs_dim = env_spec.observation_space.flat_dim
      self._action_dim = env_spec.action_space.flat_dim
      self._module = MLPModule(
          input_dim=self._obs_dim, output_dim=self._action_dim, **kwargs
      )
  
file_path: garage/src/garage/torch/policies/deterministic_mlp_policy.py
imports: import akro
  import numpy
  import torch
  from garage.torch import global_device
  from garage.torch.modules import MLPModule
  from garage.torch.policies.policy import Policy
label_desc: Use image node_id 1 to create a new DeterministicMLPPolicy object from inherited base classes: Policy with example: obj = DeterministicMLPPolicy(env_spec, name)