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

• URL: http://arxiv.org/abs/2404.01794v1
• Date: Tue, 2 Apr 2024 09:55:30 GMT
• Title: Imitation Game: A Model-based and Imitation Learning Deep Reinforcement Learning Hybrid
• Authors: Eric MSP Veith, Torben Logemann, Aleksandr Berezin, Arlena Wellßow, Stephan Balduin,
• Abstract summary: 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. 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.
• Score: 39.58317527488534
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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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