@Article{cmes.2019.05807,
AUTHOR = {Han Li, Tianding Chen, Hualiang Teng, Yingtao Jiang},
TITLE = {A Graph-Based Reinforcement Learning Method with Converged State Exploration and Exploitation},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {118},
YEAR = {2019},
NUMBER = {2},
PAGES = {253--274},
URL = {http://www.techscience.com/CMES/v118n2/33894},
ISSN = {1526-1506},
ABSTRACT = {In any classical value-based reinforcement learning method, an agent, despite of its continuous interactions with the environment, is yet unable to quickly generate a complete and independent description of the entire environment, leaving the learning method to struggle with a difficult dilemma of choosing between the two tasks, namely exploration and exploitation. This problem becomes more pronounced when the agent has to deal with a dynamic environment, of which the configuration and/or parameters are constantly changing. In this paper, this problem is approached by first mapping a reinforcement learning scheme to a directed graph, and the set that contains all the states already explored shall continue to be exploited in the context of such a graph. We have proved that the two tasks of exploration and exploitation eventually converge in the decision-making process, and thus, there is no need to face the exploration vs. exploitation tradeoff as all the existing reinforcement learning methods do. Rather this observation indicates that a reinforcement learning scheme is essentially the same as searching for the shortest path in a dynamic environment, which is readily tackled by a modified Floyd-Warshall algorithm as proposed in the paper. The experimental results have confirmed that the proposed graph-based reinforcement learning algorithm has significantly higher performance than both standard Q-learning algorithm and improved Q-learning algorithm in solving mazes, rendering it an algorithm of choice in applications involving dynamic environments.},
DOI = {10.31614/cmes.2019.05807}
}