# Domain Adaptation for Reinforcement Learning on the Atari

arXiv: https://arxiv.org/abs/1812.07452 · HTML: https://mlsanity.com/paper/1812.07452/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2018.

## Built on by (most cited first, 5 of 5)

- [Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation](https://mlsanity.com/paper/2411.09891/) (2024)
- [Building Manufacturing Deep Learning Models with Minimal and Imbalanced Training Data Using Domain Adaptation and Data Augmentation](https://mlsanity.com/paper/2306.00202/) (2023)
- [Cross-Domain Imitation Learning with a Dual Structure](https://mlsanity.com/paper/2006.01494/) (2020)
- [Intuitive Physics Guided Exploration for Sample Efficient Sim2real Transfer](https://mlsanity.com/paper/2104.08795/) (2021)
- [Domain Adapting Deep Reinforcement Learning for Real-world Speech Emotion Recognition](https://mlsanity.com/paper/2207.12248/) (2022)

## Abstract (arXiv)

Deep reinforcement learning agents have recently been successful across a variety of discrete and continuous control tasks; however, they can be slow to train and require a large number of interactions with the environment to learn a suitable policy. This is borne out by the fact that a reinforcement learning agent has no prior knowledge of the world, no pre-existing data to depend on and so must devote considerable time to exploration. Transfer learning can alleviate some of the problems by leveraging learning done on some source task to help learning on some target task. Our work presents an algorithm for initialising the hidden feature representation of the target task. We propose a domain adaptation method to transfer state representations and demonstrate transfer across domains, tasks and action spaces. We utilise adversarial domain adaptation ideas combined with an adversarial autoencoder architecture. We align our new policies' representation space with a pre-trained source policy, taking target task data generated from a random policy. We demonstrate that this initialisation step provides significant improvement when learning a new reinforcement learning task, which highlights the wide applicability of adversarial adaptation methods; even as the task and label/action space also changes.