Safe reinforcement learning for multi-energy management systems with known constraint functions. (arXiv:2207.03830v4 [eess.SY] UPDATED)

Reinforcement learning (RL) is a promising optimal control technique for
multi-energy management systems. It does not require a model a priori -
reducing the upfront and ongoing project-specific engineering effort and is
capable of learning better representations of the underlying system dynamics.
However, vanilla RL does not provide constraint satisfaction guarantees -
resulting in various potentially unsafe interactions within its safety-critical
environment. In this paper, we present two novel safe RL methods, namely
SafeFallback and GiveSafe, where the safety constraint …

arxiv energy learning management reinforcement reinforcement learning systems