苹果发布 REVERSAL-BENCH 测试环境，用于评估无重置强化学习
REVERSAL-BENCH: A Reversibility Axis and Reset Oracle for Measuring the Reset-Free RL Cliff
苹果搞了个新测试环境叫 REVERSAL-BENCH，专门用来测强化学习里环境能不能恢复原状，对做机器人控制的人可能有参考。
苹果提出的 REVERSAL-BENCH 是一个新基准测试环境，通过参数 ρ 控制环境可逆性，并提供了重置oracle来验证状态恢复能力，覆盖了八个物理引擎中的五种设置。
REVERSAL-BENCH: A Reversibility Axis and Reset Oracle for Measuring the Reset-Free RL Cliff
A central goal of autonomous reinforcement learning is continuous policy training without external resets. However, existing paradigms largely depend on underlying environmental reversibility, a property absent in real world manipulation, where events such as pushing objects off tables or spilling granular substances cannot be undone. We introduce REVERSAL-BENCH, a benchmark that controls reversibility via a continuous parameter ρ∈ [0, 1] and provides a reset oracle, a ground-truth verification mechanism to test state recoverability across eight manipulation settings in five physics engines…
