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Amplifying the Imitation Effect for Reinforcement Learning of UCAV's Mission Execution
[article]
2019
arXiv
pre-print
This paper proposes a new reinforcement learning (RL) algorithm that enhances exploration by amplifying the imitation effect (AIE). This algorithm consists of self-imitation learning and random network distillation algorithms. We argue that these two algorithms complement each other and that combining these two algorithms can amplify the imitation effect for exploration. In addition, by adding an intrinsic penalty reward to the state that the RL agent frequently visits and using replay memory
arXiv:1901.05856v1
fatcat:qrq2zrpe4ndejeqxeotvmrcbcm