  Naively trained Deep Reinforcement Learning agents may fail to satisfy vital
safety constraints. To avoid costly retraining, we may desire to repair a
previously trained reinforcement learning agent to obviate unsafe behaviour. We
devise a counterexample-guided repair algorithm for repairing reinforcement
learning systems leveraging safety critics. The algorithm jointly repairs a
reinforcement learning agent and a safety critic using gradient-based
constrained optimisation.
