JACIII Vol.10 No.4 pp. 578-585
doi: 10.20965/jaciii.2006.p0578


Opposition-Based Reinforcement Learning

Hamid R. Tizhoosh

Pattern Analysis and Machine Intelligence Laboratory, Systems Design Engineering, University of Waterloo, 200 University Avenue West, Waterloo, Ontario, Canada N2L 3G1

September 29, 2005
November 22, 2005
July 20, 2006
reinforcement learning, Q-learning, opposite action, opposite state

Reinforcement learning is a machine intelligence scheme for learning in highly dynamic, probabilistic environments. By interaction with the environment, reinforcement agents learn optimal control policies, especially in the absence of a priori knowledge and/or a sufficiently large amount of training data. Despite its advantages, however, reinforcement learning suffers from a major drawback – high calculation cost because convergence to an optimal solution usually requires that all states be visited frequently to ensure that policy is reliable. This is not always possible, however, due to the complex, high-dimensional state space in many applications. This paper introduces opposition-based reinforcement learning, inspired by opposition-based learning, to speed up convergence. Considering opposite actions simultaneously enables individual states to be updated more than once shortening exploration and expediting convergence. Three versions of Q-learning algorithm will be given as examples. Experimental results for the grid world problem of different sizes demonstrate the superior performance of the proposed approach.

Cite this article as:
H. Tizhoosh, “Opposition-Based Reinforcement Learning,” J. Adv. Comput. Intell. Intell. Inform., Vol.10, No.4, pp. 578-585, 2006.
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Last updated on Oct. 18, 2018