1.4 KiB
1.4 KiB
title, source_url, ingested, sha256
| title | source_url | ingested | sha256 |
|---|---|---|---|
| Learning to Adapt: Representation-Based Reinforcement Learning for Multi-Task Skill Transfer | https://arxiv.org/abs/2606.12890 | 2026-06-17 | <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%.