Reset-Free Reinforcement Learning Benchmarking

This post introduces REVERSAL-BENCH, a benchmark designed to measure the impact of environmental irreversibility on reinforcement learning agents, particularly those aiming for reset-free continuous training. It quantifies a 'reversibility cliff' where agents fail as environmental reversibility decreases, and evaluates the effectiveness of safety shields in predicting and mitigating irreversible failures.