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Sample-Efficient Deep Reinforcement Learning with directed associative graph

China Communications 2021年第6期18卷 100-113页

作者： Dujia Yang Xiaowei Qin Xiaodong Xu Chensheng Li Guo Wei University of Science and Technology of China Hefei 230026China CAS Key Laboratory of Wireless-Optical Communications Hefei 230027China

Reinforcement learning can be modeled as markov decision process *** consequence,the interaction samples as well as the connection relation between them are two main types of information for ***,most of recent works on deep reinforcement learning treat samples independently either in their own episode or between *** this paper,in order to utilize more sample information,we propose another learning system based on directed associative graph(DAG).The DAG is built on all trajectories in real time,which includes the whole connection relation of all samples among all *** planning with directed edges on DAG,we offer another perspective to estimate stateaction pair,especially for the unknowns to deep neural network(DNN)as well as episodic memory(EM).Mixed loss function is generated by the three learning systems(DNN,EM and DAG)to improve the efficiency of the parameter update in the proposed *** show that our algorithm is significantly better than the state-of-the-art algorithm in performance and sample efficiency on testing ***,the convergence of our algorithm is proved in the appendix and its long-term performance as well as the effects of DAG are verified.

关键词： directed associative graph sample efficiency deep reinforcement learning

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