Learning state correspondence of reinforcement learning tasks for knowledge transfer
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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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