29 lines
1.4 KiB
Markdown
29 lines
1.4 KiB
Markdown
---
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title: "Learning to Adapt: Representation-Based Reinforcement Learning for Multi-Task Skill Transfer"
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source_url: https://arxiv.org/abs/2606.12890
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ingested: 2026-06-17
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sha256: <computed>
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---
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# Learning to Adapt: Representation-Based Reinforcement Learning for Multi-Task Skill Transfer
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**Authors:** Aryan Naveen (MIT), Haitong Ma, Haldun Balim, Na Li — Harvard SEAS
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**arXiv:** 2606.12890v1 [cs.RO] (2026-06-11)
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**8 pages, 4 figures, 1 table. NSF AI Institute + ONR.**
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## Abstract
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Proposes RepMT-SAC, a framework for multi-task RL that enables efficient knowledge sharing and robust transfer to new tasks. Uses spectral MDP decomposition to capture transferable dynamics, structuring the value function into a task-agnostic core with a minimal task-specific adjustment. Allows for strong zero-shot performance on in-distribution tasks and rapid few-shot adaptation to out-of-distribution tasks. Evaluated on quadcopter trajectory-following tasks across in-distribution and out-of-distribution contexts, outperforming baselines by up to 30%.
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## Key Concepts
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- [[rep-mt-sac|RepMT-SAC]]
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- [[spectral-mdp-decomposition|谱 MDP 分解]]
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- [[task-invariant-representation|任务不变表征]]
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- [[task-conditioned-policy|任务条件策略]]
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- [[upstream-downstream-learning|上游-下游学习]]
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- [[quadrotor-trajectory-following|四旋翼轨迹跟踪]]
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- [[soft-actor-critic|SAC]]
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