Safety Optimized Reinforcement Learning via Multi-Objective Policy Optimization

arXiv:2402.15197v1 Announce Type: cross
Abstract: Safe reinforcement learning (Safe RL) refers to a class of techniques that aim to prevent RL algorithms from violating constraints in the process of decision-making and exploration during trial and error. In this paper, a novel model-free Safe RL algorithm, formulated based on the multi-objective policy optimization framework is introduced where the policy is optimized towards optimality and safety, simultaneously. The optimality is achieved by the environment reward function that is subsequently shaped using a …

abstract aim algorithm algorithms arxiv class constraints cs.ai cs.lg cs.ro cs.sy decision eess.sy error exploration free making multi-objective novel optimization paper policy process reinforcement reinforcement learning safety type via