Knowledge Transfer in Deep Reinforcement Learning via an RL-Specific GAN-Based Correspondence Function

Knowledge Transfer in Deep Reinforcement Learning via an RL-Specific GAN-Based Correspondence Function

Deep reinforcement learning has demonstrated superhuman performance in complex decision-making tasks, but it struggles with generalization and knowledge reuse—key aspects of true intelligence. This article introduces a novel approach that modifies Cycle Generative Adversarial Networks spe...

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Bibliographic Details
Main Authors: Marko Ruman, Tatiana V. Guy
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Deep learning
Markov decision process
reinforcement learning
transfer learning
knowledge transfer
Online Access:https://ieeexplore.ieee.org/document/10752398/
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https://ieeexplore.ieee.org/document/10752398/

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