複数の状態行動価値表を用いたR学習の高速化 [in Japanese] R-learning with Multiple State-action Value Tables [in Japanese]
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We propose a method to improve the performance of R-learning, a reinforcement learning algorithm, by using multiple state-action value tables. Unlike Q- or Sarsa learning, R-learning learns a policy to maximize undiscounted rewards. Multiple state-action value tables cause substantial explorations as needed and make R-learnings to work well. Efficiency of the proposed method is verified through experiments in simulation environment.
- IEEJ Transactions on Electronics, Information and Systems
IEEJ Transactions on Electronics, Information and Systems 126(1), 72-82, 2006-01-01
The Institute of Electrical Engineers of Japan