# Cross-Domain Transfer in Reinforcement Learning using Target Apprentice

arXiv: https://arxiv.org/abs/1801.06920 · HTML: https://mlsanity.com/paper/1801.06920/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jan 2018.

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

- [Procedural Content Generation: Better Benchmarks for Transfer Reinforcement Learning](https://mlsanity.com/paper/2105.14780/) (2021)
- [Weakly Supervised Correspondence Learning](https://mlsanity.com/paper/2203.00904/) (2022)
- [Domain Adaptive Imitation Learning](https://mlsanity.com/paper/1910.00105/) (2019)
- [Adaptive Policy Transfer in Reinforcement Learning](https://mlsanity.com/paper/2105.04699/) (2021)
- [Knowledge Transfer for Cross-Domain Reinforcement Learning: A Systematic Review](https://mlsanity.com/paper/2404.17687/) (2024)
- [Transfer Learning for Prosthetics Using Imitation Learning](https://mlsanity.com/paper/1901.04772/) (2019)

## Abstract (arXiv)

In this paper, we present a new approach to Transfer Learning (TL) in Reinforcement Learning (RL) for cross-domain tasks. Many of the available techniques approach the transfer architecture as a method of speeding up the target task learning. We propose to adapt and reuse the mapped source task optimal-policy directly in related domains. We show the optimal policy from a related source task can be near optimal in target domain provided an adaptive policy accounts for the model error between target and source. The main benefit of this policy augmentation is generalizing policies across multiple related domains without having to re-learn the new tasks. Our results show that this architecture leads to better sample efficiency in the transfer, reducing sample complexity of target task learning to target apprentice learning.