Safety-informed mutations for evolutionary deep reinforcement learning
Enrico Marchesini, Christopher Amato · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2022
Evolutionary Algorithms have been combined with Deep Reinforcement Learning (DRL) to address the limitations of the two approaches while leveraging their benefits. In this paper, we discuss objective-informed mutations to bias the evolutionary population toward exploring the desired objective. We focus on Safe DRL domains to show how these mutations exploit visited unsafe states to search for safer actions. Empirical evidence on a 12 degrees of freedom locomotion benchmark and a practical navigation task, confirm that we improve the safety of the policy while maintaining comparable return with the original DRL algorithm.