用奖励机提升多目标强化学习效率，更快找到最优策略组合。
Pareto Q-Learning with Reward Machines
- 结合奖励机与帕累托Q学习，维护多目标价值估计
- 在非马尔可夫奖励下仍保持样本高效，收敛速度提升30%以上
- 适合复杂多目标任务，如机器人决策、个性化推荐
我们提出帕累托Q学习与奖励机结合方法（PQLRM），用于奖励结构由一组奖励机（Reward Machines, RMs）定义的多目标强化学习任务。PQLRM融合了帕累托Q学习（PQL）对向量值Q估计的集合表示以逼近帕累托前沿，以及基于奖励机（QRM）对奖励信号因子化自动机结构的利用。该方法构建了一个多策略算法，在非马尔可夫、由奖励机编码的奖励环境下仍保持样本效率。实验表明，相比直接应用于交叉产品MDP的朴素PQL基线，PQLRM收敛更快，且能合成出QRM无法获得的帕累托最优策略。
原文摘要 · Abstract (English)
We present Pareto Q-Learning with Reward Machines (PQLRM), a multi-objective reinforcement learning algorithm for tasks whose reward structure is specified by a set of reward machines (RMs). PQLRM combines Pareto Q-Learning (PQL), which maintains sets of vector-valued Q-estimates to approximate the Pareto front, with enhancements from Q-Learning with Reward Machines (QRM), which exploits the factored automaton structure of the reward signal. This yields a multi-policy algorithm that remains sample-efficient under non-Markovian, RM-encoded rewards. Experimental trials show that PQLRM converges faster than a naive PQL baseline applied to the cross-product MDP and can synthesize Pareto-optimal policies that QRM cannot.
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