Imitation Game: A Model-based and Imitation Learning Deep Reinforcement Learning Hybrid
<|reference_start|>Imitation Game: A Model-based and Imitation Learning Deep Reinforcement Learning Hybrid: 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.<|reference_end|>
@article{veith2024imitation,
    title={Imitation Game: A Model-based and Imitation Learning Deep Reinforcement
  Learning Hybrid},
    author={Eric MSP Veith, Torben Logemann, Aleksandr Berezin, Arlena
  Well{ss}ow, Stephan Balduin},
    journal={arXiv preprint arXiv:2404.01794},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.01794},
    primaryClass={cs.AI}
}