code: def __init__(
      self,
      algorithm,
      obs_dim,
      act_dim,
      kl_targ,
      loss_type,
      beta=1.0,
      epsilon=0.2,
      policy_learn_times=20,
      value_learn_times=10,
      value_batch_size=256,
  ):
      self.obs_dim = obs_dim
      self.act_dim = act_dim
      assert loss_type == "CLIP" or loss_type == "KLPEN"
      self.loss_type = loss_type
      super(MujocoAgent, self).__init__(algorithm)
  
      self.policy_learn_times = policy_learn_times
      # Adaptive kl penalty coefficient
      self.beta = beta
      self.kl_targ = kl_targ
  
      self.value_learn_times = value_learn_times
      self.value_batch_size = value_batch_size
      self.value_learn_buffer = None
  
file_path: PARL/benchmark/fluid/PPO/mujoco_agent.py
imports: import numpy
  import parl
  from parl import layers
  from paddle import fluid
  from parl.utils import logger
label_desc: Use image node_id 1 to create a new MujocoAgent object from inherited base classes: parl with example: obj = MujocoAgent(algorithm, obs_dim, act_dim, kl_targ, loss_type, beta, epsilon, policy_learn_times, value_learn_times, value_batch_size)