XCS with Adaptive Action Mapping
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The XCS classifier system evolves solutions that represent complete mappings from state-action pairs to expected returns therefore, in every possible situation, XCS can predict the value of all the available actions. Such complete mapping is sometimes considered redundant as most of the applications (like for instance, classification), usually focus only on the best action. In this paper, we introduce an extension of XCS with an adaptive (state-action) mapping mechanism (or XCSAM) that evolves solutions focused actions with the largest returns. While UCS evolves solutions focused on the best available action but can only solve supervised classification problems, our system can solve both supervised and multi-step problems and, in addition, it can adapt the size of the mapping to the problems: Initially, XCSAM starts building a complete mapping and then it slowly tries to focus on the best actions available. If the problem admits only one optimal action in each niche, XCSAM tends to focus on such an action as the evolution proceeds. If more actions with the same return are available, XCSAM tends to evolve a mapping that includes all of them. We applied XCSAM both to supervised problems (the Boolean multiplexer) and to multi-step maze-like problems. Our experimental results show that XCSAM can reach optimal performance but requires smaller populations than XCS as it evolves solutions focused on the best actions available for each subproblem.
KeywordsGood Action Tournament Selection Complete Mapping Reinforcement Learning Problem Redundant Action
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