# Online Transfer Learning in Reinforcement Learning Domains

arXiv: https://arxiv.org/abs/1507.00436 · HTML: https://mlsanity.com/paper/1507.00436/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2015.

## Built on by (most cited first, 6 of 6)

- [On the Sample Complexity of Representation Learning in Multi-task Bandits with Global and Local structure](https://mlsanity.com/paper/2211.15129/) (2022)
- [Collaborative Training of Heterogeneous Reinforcement Learning Agents in Environments with Sparse Rewards: What and When to Share?](https://mlsanity.com/paper/2202.12174/) (2022)
- [Online Transfer Learning: Negative Transfer and Effect of Prior Knowledge](https://mlsanity.com/paper/2105.01445/) (2021)
- [A Bayesian Approach to (Online) Transfer Learning: Theory and Algorithms](https://mlsanity.com/paper/2109.01377/) (2021)
- [Is Exploration All You Need? Effective Exploration Characteristics for Transfer in Reinforcement Learning](https://mlsanity.com/paper/2404.02235/) (2024)
- [The Role of Exploration for Task Transfer in Reinforcement Learning](https://mlsanity.com/paper/2210.06168/) (2022)

## Abstract (arXiv)

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.