Inverse Reinforcement Learning based on Critical State
نویسندگان
چکیده
Inverse reinforcement learning is tried to search a reward function based on Markov Decision Process. In the IRL topics, experts produce some good traces to make agents learn and adjust the reward function. But the function is difficult to set in some complicate problems. In this paper, Inverse Reinforcement Learning based on Critical State (IRLCS) is proposed to search a succinct and meaningful reward function. IRLCS select a set of reward indexes from whole state space through comparing the difference between the good and bad demonstrations. According to the simulation results, IRLCS can search a good strategy that is similar to experts.
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تاریخ انتشار 2015