class_docstr: ϵ-greedy exploration strategy.
  
  Select action based on the value of ϵ. ϵ will decrease from
  max_epsilon to min_epsilon within decay_ratio * total_timesteps.
  
  At state s, with probability
  1 − ϵ: select action = argmax Q(s, a)
  ϵ    : select a random action from an uniform distribution.
  
  Args:
      env_spec (garage.envs.env_spec.EnvSpec): Environment specification.
      policy (garage.Policy): Policy to wrap.
      total_timesteps (int): Total steps in the training, equivalent to
          max_episode_length * n_epochs.
      max_epsilon (float): The maximum(starting) value of epsilon.
      min_epsilon (float): The minimum(terminal) value of epsilon.
      decay_ratio (float): Fraction of total steps for epsilon decay.
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docstr: Set param values.
  
  Args:
      params (np.ndarray): A numpy array of parameter values.
docstr_tok: ["Set","param","values",".","Args",":","params","(","np.ndarray",")",":","A","numpy","array","of","parameter","values","."]
code: def set_param_values(self, params):
      """Set param values.
  
      Args:
          params (np.ndarray): A numpy array of parameter values.
  
      """
      self._total_env_steps = params["total_env_steps"]
      self.policy.set_param_values(params["inner_params"])
      self._last_total_env_steps = self._total_env_steps
  
file_path: garage/src/garage/np/exploration_policies/epsilon_greedy_policy.py
imports: from dowel import tabular
  import numpy
  from garage.np.exploration_policies.exploration_policy import ExplorationPolicy
label_desc: Use image node_id 7 for calling the EpsilonGreedyPolicy obj's underlying member method code with example usage: obj.set_param_values(params) without return types