---
title: "Learning to Adapt: Representation-Based Reinforcement Learning for Multi-Task Skill Transfer"
source_url: https://arxiv.org/abs/2606.12890
ingested: 2026-06-17
sha256: <computed>
---

# Learning to Adapt: Representation-Based Reinforcement Learning for Multi-Task Skill Transfer

**Authors:** Aryan Naveen (MIT), Haitong Ma, Haldun Balim, Na Li — Harvard SEAS

**arXiv:** 2606.12890v1 [cs.RO] (2026-06-11)

**8 pages, 4 figures, 1 table. NSF AI Institute + ONR.**

## Abstract

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%.

## Key Concepts

- [[rep-mt-sac|RepMT-SAC]]
- [[spectral-mdp-decomposition|谱 MDP 分解]]
- [[task-invariant-representation|任务不变表征]]
- [[task-conditioned-policy|任务条件策略]]
- [[upstream-downstream-learning|上游-下游学习]]
- [[quadrotor-trajectory-following|四旋翼轨迹跟踪]]
- [[soft-actor-critic|SAC]]
