Model-based reinforcement learning with missing data

Nobuhiko Yamaguchi, Osamu Fukuda, Hiroshi Okumura

Abstract: Model-based reinforcement learning is a powerful paradigm for learning tasks in robotics. However, real world learning tasks often involve complex patterns of missing data, and model-based reinforcement learning cannot handle missing data directly. To overcome this problem, in this paper, we focus on M-PGPE(GP) proposed by Mori et al. as a model-based reinforcement learning, and propose an extension of M-PGPE(GP) to handle missing data, which we call MM-PGPE(GP). The performance of the proposed MM-PGPE(GP) is assessed in two experiments with mountain car task. These experiments highlight the MM-PGPE(GP) produces higher average return and outperforms the conventional M-PGPE(GP) with simple linear interpolation.