DEMO: Self-Preserving Genetic Algorithms vs. Safe Reinforcement Learning in Discrete Action Spaces

Abstract

Safe learning techniques are learning frameworks that take safety into consideration during the training process. Safe reinforcement learning (SRL) combines reinforcement learning (RL) with safety mechanisms such as action masking and run time assurance to protect an agent during the exploration of its environment. This protection, though, can severely hinder an agent's ability to learn optimal policies as the safety systems exacerbate an already difficult exploration challenge for RL agents. An alternative to RL is an optimization approach known as genetic algorithms (GA), which utilize operators that mimic biological evolution to evolve better policies. By combining safety mechanisms with genetic algorithms, this work demonstrates a novel approach to safe learning called Self-Preserving Genetic Algorithms.