Web: http://arxiv.org/abs/2209.06604

Sept. 15, 2022, 1:11 a.m. | Marko Ruman, Tatiana V. Guy

cs.LG updates on arXiv.org arxiv.org

Deep reinforcement learning has shown an ability to achieve super-human
performance in solving complex reinforcement learning (RL) tasks only from
raw-pixels. However, it fails to reuse knowledge from previously learnt tasks
to solve new, unseen ones. Generalizing and reusing knowledge are the
fundamental requirements for creating a truly intelligent agent. This work
proposes a general method for one-to-one transfer learning based on generative
adversarial network model tailored to RL task.

arxiv knowledge reinforcement reinforcement learning state transfer

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