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To be applicable to real world problems, much reinforcement learning (RL) research has focused on continuous state spaces with function approximations. Some problems also require continuous actions, but searching for good actions in a continuous action space is problematic. This paper suggests a novel relevance vector sampling approach to action search in an RL framework with relevance vector machines...
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