docstr: Double DQN algorithm
  
  Args:
      model (parl.Model): model defining forward network of Q function.
      gamma (float): discounted factor for reward computation.
docstr_tok: ["Double","DQN","algorithm","Args",":","model","(","parl.Model",")",":","model","defining","forward","network","of","Q","function",".","gamma","(","float",")",":","discounted","factor","for","reward","computation","."]
code: def __init__(self, model, act_dim=None, gamma=None, lr=None):
      """Double DQN algorithm
  
      Args:
          model (parl.Model): model defining forward network of Q function.
          gamma (float): discounted factor for reward computation.
      """
      self.model = model
      self.target_model = copy.deepcopy(model)
  
      assert isinstance(act_dim, int)
      assert isinstance(gamma, float)
  
      self.act_dim = act_dim
      self.gamma = gamma
      self.lr = lr
  
file_path: PARL/benchmark/fluid/Prioritized_DQN/per_alg.py
imports: import copy
  import numpy
  import paddle.fluid
  import parl
  from parl.core.fluid import layers
label_desc: Use image node_id 1 to create a new PrioritizedDoubleDQN object from inherited base classes: parl with example: obj = PrioritizedDoubleDQN(model, act_dim, gamma, lr)