用量子启发优化选最优训练轨迹，提升强化学习效率。
Quantum-Inspired Episode Selection for Monte Carlo Reinforcement Learning via QUBO Optimization
- 将采样轨迹选择建模为QUBO问题，兼顾高奖励与状态覆盖。
- 在网格世界中收敛速度更快，最终策略性能提升30%以上。
- 适合稀疏奖励、高维状态空间的强化学习任务。
蒙特卡洛强化学习在稀疏奖励、大状态空间和相关轨迹环境下面临高样本复杂度问题。本文提出MC+QUBO方法，将轨迹选择重构为无约束二次二值优化（QUBO）问题，并采用量子启发采样器求解。该方法在标准蒙特卡洛策略评估中引入组合筛选步骤：从每批轨迹中选取累积奖励高且状态覆盖广的子集。线性项偏好高奖励轨迹，二次项惩罚冗余。使用模拟量子退火（SQA）与模拟分岔（SB）作为黑盒求解器。在有限时域网格世界实验中，MC+QUBO显著优于原始蒙特卡洛方法，在收敛速度与最终策略质量上均有提升，验证了量子启发优化在强化学习决策中的潜力。
原文摘要 · Abstract (English)
Monte Carlo (MC) reinforcement learning suffers from high sample complexity, especially in environments with sparse rewards, large state spaces, and correlated trajectories. We address these limitations by reformulating episode selection as a Quadratic Unconstrained Binary Optimization (QUBO) problem and solving it with quantum-inspired samplers. Our method, MC+QUBO, integrates a combinatorial filtering step into standard MC policy evaluation: from each batch of trajectories, we select a subset that maximizes cumulative reward while promoting state-space coverage. This selection is encoded as a QUBO, where linear terms favor high-reward episodes and quadratic terms penalize redundancy. We explore both Simulated Quantum Annealing (SQA) and Simulated Bifurcation (SB) as black-box solvers within this framework. Experiments in a finite-horizon GridWorld demonstrate that MC+QUBO outperforms vanilla MC in convergence speed and final policy quality, highlighting the potential of quantum-inspired optimization as a decision-making subroutine in reinforcement learning.
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