Imitation Game: A Model-based and Imitation Learning Deep Reinforcement Learning Hybrid

Eric MSP Veith; Torben Logemann; Aleksandr Berezin; Arlena Wellßow; Stephan Balduin

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arXiv · 2404.01794

Imitation Game: A Model-based and Imitation Learning Deep Reinforcement Learning Hybrid

Eric MSP Veith·Torben Logemann·Aleksandr Berezin·Arlena Wellßow·Stephan Balduin

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Abstract

Autonomous and learning systems based on Deep Reinforcement Learning have firmly established themselves as a foundation for approaches to creating resilient and efficient Cyber-Physical Energy Systems. However, most current approaches suffer from two distinct problems: Modern model-free algorithms such as Soft Actor Critic need a high number of samples to learn a meaningful policy, as well as a fallback to ward against concept drifts (e. g., catastrophic forgetting). In this paper, we present the work in progress towards a hybrid agent architecture that combines model-based Deep Reinforcement Learning with imitation learning to overcome both problems.

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Eric MSP Veith, Torben Logemann, Aleksandr Berezin, Arlena Wellßow, Stephan Balduin. 2024-04-02. Imitation Game: A Model-based and Imitation Learning Deep Reinforcement Learning Hybrid. https://arxiv.org/abs/2404.01794

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