# Multi-View Reinforcement Learning

arXiv: https://arxiv.org/abs/1910.08285 · HTML: https://mlsanity.com/paper/1910.08285/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2019.

## Built on by (most cited first, 12 of 12)

- [When Physics Meets Machine Learning: A Survey of Physics-Informed Machine Learning](https://mlsanity.com/paper/2203.16797/) (2022)
- [DRIBO: Robust Deep Reinforcement Learning via Multi-View Information Bottleneck](https://mlsanity.com/paper/2102.13268/) (2021)
- [Uncertainty Estimation Using Riemannian Model Dynamics for Offline Reinforcement Learning](https://mlsanity.com/paper/2102.11327/) (2021)
- [Efficient Semi-Implicit Variational Inference](https://mlsanity.com/paper/2101.06070/) (2021)
- [Addressing Action Oscillations through Learning Policy Inertia](https://mlsanity.com/paper/2103.02287/) (2021)
- [Multi-View Dreaming: Multi-View World Model with Contrastive Learning](https://mlsanity.com/paper/2203.11024/) (2022)
- [Similarity-based Knowledge Transfer for Cross-Domain Reinforcement Learning](https://mlsanity.com/paper/2312.03764/) (2023)
- [Robust Multi-agent Communication via Multi-view Message Certification](https://mlsanity.com/paper/2305.13936/) (2023)
- [Merging and Disentangling Views in Visual Reinforcement Learning for Robotic Manipulation](https://mlsanity.com/paper/2505.04619/) (2025)
- [Tackling Visual Control via Multi-View Exploration Maximization](https://mlsanity.com/paper/2211.15233/) (2022)
- [Learning Fused State Representations for Control from Multi-View Observations](https://mlsanity.com/paper/2502.01316/) (2025)
- [From Causal Graphs to Markovian States: Multi-Order State Exposure for Deep RL](https://mlsanity.com/paper/2605.07057/) (2026)

## Abstract (arXiv)

This paper is concerned with multi-view reinforcement learning (MVRL), which allows for decision making when agents share common dynamics but adhere to different observation models. We define the MVRL framework by extending partially observable Markov decision processes (POMDPs) to support more than one observation model and propose two solution methods through observation augmentation and cross-view policy transfer. We empirically evaluate our method and demonstrate its effectiveness in a variety of environments. Specifically, we show reductions in sample complexities and computational time for acquiring policies that handle multi-view environments.