A Study of Qualitative Knowledge-Based Exploration for Continuous Deep Reinforcement Learning

Chenxi LI  Lei CAO  Xiaoming LIU  Xiliang CHEN  Zhixiong XU  Yongliang ZHANG  

IEICE TRANSACTIONS on Information and Systems   Vol.E100-D   No.11   pp.2721-2724
Publication Date: 2017/11/01
Publicized: 2017/07/26
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2017EDL8112
Type of Manuscript: LETTER
Category: Artificial Intelligence, Data Mining
deep reinforcement learning,  knowledge,  exploration strategy,  cloud control systems,  

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As an important method to solve sequential decision-making problems, reinforcement learning learns the policy of tasks through the interaction with environment. But it has difficulties scaling to large-scale problems. One of the reasons is the exploration and exploitation dilemma which may lead to inefficient learning. We present an approach that addresses this shortcoming by introducing qualitative knowledge into reinforcement learning using cloud control systems to represent ‘if-then’ rules. We use it as the heuristics exploration strategy to guide the action selection in deep reinforcement learning. Empirical evaluation results show that our approach can make significant improvement in the learning process.