Logic-Guided Eligibility Traces for Delayed and Sparse Reward Reinforcement Learning

Delayed and sparse rewards present a fundamental challenge in reinforcement learning, degrading performance due to impaired temporal credit assignment. Traditional eligibility traces and multi-step returns propagate learning signals backward in time but implicitly assume timely reward observation, limiting effectiveness under delayed feedback. We propose Logic-Guided Eligibility Traces (LGET), a neuro-symbolic framework that integrates symbolic logical inference into the eligibility trace mechan