论文信息 - Robust, Efficient, Globally-Optimized Reinforcement Learning with the Parti-Game Algorithm

Robust, Efficient, Globally-Optimized Reinforcement Learning with the Parti-Game Algorithm

Parti-game (Moore 1994a; Moore 1994b; Moore and Atkeson 1995) is a reinforcement learning (RL) algorithm that has a lot of promise in overcoming the curse of dimensionality that can plague RL algorithms when applied to high-dimensional problems. In this paper we introduce modifications to the algorithm that further improve its performance and robustness. In addition, while parti-game solutions can be improved locally by standard local path-improvement techniques, we introduce an add-on algorithm in the same spirit as parti-game that instead tries to improve solutions in a non-local manner.

Ronald J. Williams | Mohammad A. Al-Ansari | Ronald J. Williams | M. A. Al-Ansari

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