# Adaptive 3D UI Placement in Mixed Reality Using Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2504.21731 · HTML: https://mlsanity.com/paper/2504.21731/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL with diffusion and flow models, one of 15 directions in Deep reinforcement learning methods.
- Builds on 15 of its 47 references that are in the map.
- First posted on arXiv 30 Apr 2025.

## Placed in

- Deep reinforcement learning methods / [RL with diffusion and flow models](https://mlsanity.com/deeprl/rl-with-diffusion-and-flow-models/)

## Builds on (most cited first, 15 of 15)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Dota 2 with Large Scale Deep Reinforcement Learning](https://mlsanity.com/paper/1912.06680/) (2019)
- [The Replica Dataset: A Digital Replica of Indoor Spaces](https://mlsanity.com/paper/1906.05797/) (2019)
- [Model-Based Reinforcement Learning for Atari](https://mlsanity.com/paper/1903.00374/) (2019)
- [Unity: A General Platform for Intelligent Agents](https://mlsanity.com/paper/1809.02627/) (2018)
- [Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning](https://mlsanity.com/paper/1809.08835/) (2018)
- [Few-Shot Preference Learning for Human-in-the-Loop RL](https://mlsanity.com/paper/2212.03363/) (2022)
- [Chip Placement with Deep Reinforcement Learning](https://mlsanity.com/paper/2004.10746/) (2020)
- [Adapting User Interfaces with Model-based Reinforcement Learning](https://mlsanity.com/paper/2103.06807/) (2021)
- [On the Use and Misuse of Absorbing States in Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2111.05992/) (2021)
- [GRIDS: Interactive Layout Design with Integer Programming](https://mlsanity.com/paper/2001.02921/) (2020)
- [External Labeling Techniques: A Taxonomy and Survey](https://mlsanity.com/paper/1902.01454/) (2019)
- [Context-Responsive Labeling in Augmented Reality](https://mlsanity.com/paper/2102.07735/) (2021)

## Built on by (most cited first, 1 of 1)

- [Inverse Theory of Mind Modeling for Content Recommendation: From Web Browsing to Dynamic Intelligent Interfaces](https://mlsanity.com/paper/2608.11354/) (2026)

## Abstract (arXiv)

Mixed Reality (MR) could assist users' tasks by continuously integrating virtual content with their view of the physical environment. However, where and how to place these content to best support the users has been a challenging problem due to the dynamic nature of MR experiences. In contrast to prior work that investigates optimization-based methods, we are exploring how reinforcement learning (RL) could assist with continuous 3D content placement that is aware of users' poses and their surrounding environments. Through an initial exploration and preliminary evaluation, our results demonstrate the potential of RL to position content that maximizes the reward for users on the go. We further identify future directions for research that could harness the power of RL for personalized and optimized UI and content placement in MR.