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Reinforcement learning is a useful tool for complex control problems that cannot be modeled mathematically nor solved theoretically. Direct policy search(DPS) is an approach for reinforcement learning that represents a policy using some model and searches an optimal parameter directly by optimization techniques such as genetic algorithms(GA). Instance-based policy is a policy representation model...
Real-time reinforcement learning is difficult because number of episodes is too much to complete learning within limited time in practice. On the other hand, in spite of trial-and-error learning, animals can complete learning within limited time. Conventional framework cannot explain it. In this paper, we address the pursuit problem using optical information tau and information of direction that is...
We present a model-based RL approach to cope with continuous space of high D.O.F. robots, combining model learning and an actor-critic method. The model learner generates a discrete state-transition model that helps improvement of both the policy and state-representation. In general, model-based methods tends to fail in non-Markovian problems, but the proposed method, using actor-critic, can find...
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