Imitation Game: A Model-based and Imitation Learning Deep Reinforcement Learning Hybrid
This is an incremental approach aimed at improving resilience and efficiency in cyber-physical energy systems.
The paper tackles the high sample inefficiency and lack of fallback mechanisms in model-free deep reinforcement learning for cyber-physical energy systems by proposing a hybrid agent architecture combining model-based DRL with imitation learning, but no concrete results or numbers are provided as it is a work in progress.
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.