算法伪代码 : 5. 实验结果 6.算法实现 (仅在部分Atari游戏中使⽤) 本部分代码包含两种算法 NoisyNet-DQN,NoisyNEt-A3C (1)NoisyNet-DQN # code source: / wenh 123/ NoisyNet-DQN/blob/master/ train.py import argparse import gym import numpy as np import os import tensorflow as tf import ...
采样过程的实现如下: 代码语言:javascript 复制 defsample(self,n):b_idx,b_memory,ISWeights=np.empty((n,),dtype=np.int32),np.empty((n,self.tree.data[0].size)),np.empty((n,1))pri_seg=self.tree.total_p/n # priority segment self.beta=np.min([1.,self.beta+self.beta_increment_per_...
(steps_per_iter) / (float(iteration_time_est) + 1e-6) if steps_per_iter._value is not None else "calculating...") logger.dump_tabular() logger.log() logger.log("ETA: " + pretty_eta(int(steps_left / fps_estimate))) logger.log() # add summary for one episode ep_stats.add_...