Learning state correspondence of reinforcement learning tasks for knowledge transfer

• URL: http://arxiv.org/abs/2209.06604v1
• Date: Wed, 14 Sep 2022 12:42:59 GMT
• Title: Learning state correspondence of reinforcement learning tasks for knowledge transfer
• Authors: Marko Ruman and Tatiana V. Guy
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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