Technical Note: \cal Q-Learning
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Approximate Q-Learning: An Introduction
ICMLC '10 Proceedings of the 2010 Second International Conference on Machine Learning and Computing
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This paper introduces an approach to Reinforcement Learning Algorithm by introducing reduct concept of rough set methodology using a variation of Q-Learning algorithm. Unlike the conventional Q-Learning, the proposed algorithm calculates the reduct from look up table of previous episodes. In modified algorithm first action selection of an agent will based upon the reduct of previous episode. In Q-learning algorithm an agent makes action selections in an effort to maximize a return obtained from the environment. Agent will changes its policy for future actions based upon return. The problem considered in this paper is convergence of Q-value that takes more episodes in conventional Q-learning. The solution to this problem results from a new form Q-learning algorithm by calculating the reduct of previous episodes. The framework provided by a reduct based Q-Learning algorithm in order to simplify the learned moves and interested in eliminating conditional moves of an Agent. Effectiveness of proposed algorithm is simulated in a 10 x 10 Grid world deterministic environment and the result for the two forms of Q-Learning Algorithms is given.