Stochastic Initial States Randomization Method for Robust Knowledge Transfer in Multi-Agent Reinforcement Learning
Dohyun Kim,
Jungho Bae
Abstract:Reinforcement learning, which are also studied in the field of defense, face the problem of sample efficiency, which requires a large amount of data to train. Transfer learning has been introduced to address this problem, but its effectiveness is sometimes marginal because the model does not effectively leverage prior knowledge. In this study, we propose a stochastic initial state randomization(SISR) method to enable robust knowledge transfer that promote generalized and sufficient knowledge transfer. We devel… Show more
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