通过谱分解实现多任务强化学习的高效迁移，提升新任务适应能力。
Learning to Adapt: Representation-Based Reinforcement Learning for Multi-Task Skill Transfer
- 用谱马尔可夫决策过程分解动态，分离通用与特定任务部分。
- 在同分布任务上零样本性能强，在跨分布任务上仅需少量样本即可快速适应。
- 适用于需要快速迁移复杂控制策略的机器人系统场景。
强化学习在复杂控制策略学习中取得显著进展，但受限于样本效率低和任务间泛化能力差。本文提出RepMT-SAC框架，支持多任务强化学习中的高效知识共享与鲁棒迁移。该方法利用谱马尔可夫决策过程（spectral MDP）分解，提取可迁移的动态特性，将价值函数结构化为任务无关的核心部分与最小化任务特异性调整。这一设计使模型在分布内任务上具备强零样本性能，在分布外任务上能实现快速少样本适应。我们在四旋翼飞行器轨迹跟踪任务上进行评估，涵盖分布内与分布外情境，结果表明其性能优于基线方法最高达30%。
原文摘要 · Abstract (English)
Reinforcement learning has achieved remarkable success in learning complex control policies, yet its applicability remains limited due to sample inefficiency and poor generalization across tasks. In this work, we propose RepMT-SAC, a framework for multi-task RL that enables efficient knowledge sharing and robust transfer to new tasks. RepMT-SAC uses spectral MDP decomposition to capture transferable dynamics, structuring the value function into a task-agnostic core with a minimal task-specific adjustment. This design allows for strong zero-shot performance on in-distribution tasks and rapid few-shot adaptation to out-of-distribution tasks. We evaluate RepMT-SAC on quadcopter trajectory-following tasks across in-distribution and out-of-distribution contexts, demonstrating that it outperforms baselines by up to 30%.
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