Imitation Game: A Model-based and Imitation Learning Deep Reinforcement Learning Hybrid
arxiv(2024)
摘要
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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