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We propose to incrementally learn the segmentation of a demonstrated task into subtasks and the individual subtask policies themselves simultaneously. Previous robot learning from demonstration techniques have either learned the individual subtasks in isolation, combined known subtasks, or used knowledge of the overall task structure to perform segmentation. Our infinite mixture of experts approach...
In this paper, we study several adaptive learning strategies for robot agents in a Robocop game. A Q-learning based method is introduced to learning the mapping among agentpsilas actions. We apply these strategies to improve robotpsilas plan. In order to facilitate the development of shred understanding among game strategies, Pigetpsilas cognitive theory is applied to the use of cooperative learning...
The Multi-stage Genetic Algorithm, MGA, is introduced to solve a class of compositional design problems. The problem with complicated constraints is formulated as a set of local subproblems with simple constraints and a supervising problem. Every subproblem is solved by GA to generate a set of suboptimal solutions. And in the supervising problem, the elements of each set are optimally combined by...
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