class_docstr: Actor that acts according to the Q-function.
class_docstr_tok: ["Actor","that","acts","according","to","the","Q-function","."]
code: def __init__(
      self,
      pipe,
      model,
      explorer,
      phi=lambda x: x,
      recurrent=False,
      logger=getLogger(__name__),
      batch_states=batch_states,
  ):
      self.pipe = pipe
      self.model = model
      self.explorer = explorer
      self.phi = phi
      self.recurrent = recurrent
      self.logger = logger
      self.batch_states = batch_states
  
      self.t = 0
      self.last_state = None
      self.last_action = None
  
      # Recurrent states of the model
      self.train_recurrent_states = None
      self.train_prev_recurrent_states = None
      self.test_recurrent_states = None
  
file_path: pfrl/pfrl/agents/state_q_function_actor.py
imports: from logging import getLogger
  import torch
  from pfrl import agent
  from pfrl.utils import evaluating
  from pfrl.utils.batch_states import batch_states
  from pfrl.utils.recurrent import get_recurrent_state_at, one_step_forward, recurrent_state_as_numpy
label_desc: Use image node_id 1 to create a new StateQFunctionActor object from inherited base classes: agent with example: obj = StateQFunctionActor(pipe, model, explorer, phi, recurrent, logger, batch_states)