• 人工智能 •

### 解决深度探索问题的贝叶斯深度强化学习算法

1. 中南大学 信息科学与工程学院，长沙 410083
• 出版日期:2020-02-01 发布日期:2020-02-16

### Bayesian Deep Reinforcement Learning Algorithm for Solving Deep Exploration Problems

YANG Min, WANG Jie

1. College of Information Science and Engineering, Central South University, Changsha 410083, China
• Online:2020-02-01 Published:2020-02-16

Abstract:

In the field of reinforcement learning, how to balance the relationship between exploration and exploi-tation is a hard problem. The reinforcement learning method proposed in recent years mainly focuses on how to combine the deep learning technology to improve the generalization ability of the algorithm, but ignores the explo-ration-exploitation dilemma. The traditional reinforcement learning method can effectively solve the exploration problem, but there are certain restrictions: the state space of the Markov decision process must be discrete and limited. In this paper, the Bayesian method is proposed to improve the efficiency of deep reinforcement algorithm. And the main contribution is to extend the method of calculating the posterior distribution of parameters in Bayesian linear regression to nonlinear models such as artificial neural networks. By combining Bootstrapped DQN (deep Q-network) and the computational method proposed in this paper, Bayesian Bootstrapped DQN (BBDQN) is obtained. Finally, the results of the experiments in two environments show that BBDQN is more efficient than DQN and Bootstrapped DQN in the face of deep exploration.