Online Transfer Learning in Reinforcement Learning Domains

2015-07-02 · Yusen Zhan, Matthew E. Taylor

This paper proposes an online transfer framework to capture the interaction among agents and shows that current transfer learning in reinforcement learning is a special case of online transfer. Furthermore, this paper re-characterizes existing agents-teaching-agents methods as online transfer and analyze one such teaching method in three ways. First, the convergence of Q-learning and Sarsa with tabular representation with a finite budget is proven. Second, the convergence of Q-learning and Sarsa with linear function approximation is established. Third, the we show the asymptotic performance cannot be hurt through teaching. Additionally, all theoretical results are empirically validated.

📄 PDF Abstract BibTeX arXiv:1507.00436

Code (0)

등록된 구현이 없습니다.

Tasks

Q-Learningreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)Transfer Learning

Methods 이 논문이 사용한 방법론

Sarsa Sarsa is an on-policy TD control algorithm: $$Q\left(S\_{t}, A\_{t}\right) \leftarrow Q\left(S\_{t}, A\_{t}\right) + \alpha\left[R_{t+1} + \gamma{Q}\left(S\_{t+1},…

Q-Learning Q-Learning is an off-policy temporal difference control algorithm: $$Q\left(S\_{t}, A\_{t}\right) \leftarrow Q\left(S\_{t}, A\_{t}\right) + \alpha\left[R_{t+1} +…

HoRD: Robust Humanoid Control via History-Conditioned Reinforcement Learning and Online Distillation

2026-02-04 · Puyue Wang, Jiawei Hu, Yan Gao, Junyan Wang 외 arxiv

Humanoid robots can suffer significant performance drops under small changes in dynamics, task specifications, or environment setup. We propose HoRD, a two-stage learning framework for robust humanoid control under domai…

Reinforcement Learning

Transferring Expectations in Model-based Reinforcement Learning

2012-12-01 · NeurIPS 2012 12 · Trung Nguyen, Tomi Silander, Tze Y. Leong

We study how to automatically select and adapt multiple abstractions or representations of the world to support model-based reinforcement learning. We address the challenges of transfer learning in heterogeneous environm…

model Model-based Reinforcement Learning reinforcement-learning Reinforcement Learning+2

Homogeneous Online Transfer Learning with Online Distribution Discrepancy Minimization

2019-12-31 · Yuntao Du, Zhiwen Tan, Qian Chen, Yi Zhang 외

Transfer learning has been demonstrated to be successful and essential in diverse applications, which transfers knowledge from related but different source domains to the target domain. Online transfer learning(OTL) is a…

Transfer Learning

Decoupling Dynamics and Reward for Transfer Learning

2018-04-27 · Amy Zhang, Harsh Satija, Joelle Pineau

Current reinforcement learning (RL) methods can successfully learn single tasks but often generalize poorly to modest perturbations in task domain or training procedure. In this work, we present a decoupled learning stra…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)Transfer Learning

Towards Task-Prioritized Policy Composition

2022-09-20 · Finn Rietz, Erik Schaffernicht, Todor Stoyanov, Johannes A. Stork

Combining learned policies in a prioritized, ordered manner is desirable because it allows for modular design and facilitates data reuse through knowledge transfer. In control theory, prioritized composition is realized …