A Graph-Based Reinforcement Learning Method with Converged State Exploration and Exploitation

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.