Learning and Planning with a Semantic Model
arXiv: Learning, Volume abs/1809.10842, 2018.
Building deep reinforcement learning agents that can generalize and adapt to unseen environments remains a fundamental challenge for AI. This paper describes progresses on this challenge in the context of man-made environments, which are visually diverse but contain intrinsic semantic regularities. We propose a hybrid model-based and mode...More
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