查看文章

Reinforcement learning is a machine intelligence scheme for learning in highly dynamic and probabilistic environments. The methodology, however, suffers from a major drawback; the convergence to an optimal solution usually requires high computational expense since all states should be visited frequently in order to guarantee a reliable policy. In this paper, a new reinforcement learning algorithm is introduced to achieve a faster convergence by taking into account the opposite actions. By considering the opposite actions simultaneously multiple updates can be made for each state observation. This leads to a shorter exploration period and, hence, expedites the convergence. Experimental results for the grid world problem of different sizes are provided to verify the performance of the proposed approach.