# DRIBO: Robust Deep Reinforcement Learning via Multi-View Information Bottleneck

arXiv: https://arxiv.org/abs/2102.13268 · HTML: https://mlsanity.com/paper/2102.13268/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 51 references that are in the map.
- First posted on arXiv 26 Feb 2021.

## Builds on (most cited first, 30 of 36)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [Dream to Control: Learning Behaviors by Latent Imagination](https://mlsanity.com/paper/1912.01603/) (2019)
- [Learning deep representations by mutual information estimation and maximization](https://mlsanity.com/paper/1808.06670/) (2018)
- [Learning Latent Dynamics for Planning from Pixels](https://mlsanity.com/paper/1811.04551/) (2018)
- [Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer](https://mlsanity.com/paper/1612.03928/) (2016)
- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [Mastering Atari with Discrete World Models](https://mlsanity.com/paper/2010.02193/) (2020)
- [DeepMind Control Suite](https://mlsanity.com/paper/1801.00690/) (2018)
- [End-to-End Training of Deep Visuomotor Policies](https://mlsanity.com/paper/1504.00702/) (2015)
- [CURL: Contrastive Unsupervised Representations for Reinforcement Learning](https://mlsanity.com/paper/2004.04136/) (2020)
- [MINE: Mutual Information Neural Estimation](https://mlsanity.com/paper/1801.04062/) (2018)
- [Data-Efficient Image Recognition with Contrastive Predictive Coding](https://mlsanity.com/paper/1905.09272/) (2019)
- [On Variational Bounds of Mutual Information](https://mlsanity.com/paper/1905.06922/) (2019)
- [Reinforcement Learning with Augmented Data](https://mlsanity.com/paper/2004.14990/) (2020)
- [Learning Invariant Representations for Reinforcement Learning without Reconstruction](https://mlsanity.com/paper/2006.10742/) (2020)
- [Quantifying Generalization in Reinforcement Learning](https://mlsanity.com/paper/1812.02341/) (2018)
- [Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images](https://mlsanity.com/paper/1506.07365/) (2015)
- [Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model](https://mlsanity.com/paper/1907.00953/) (2019)
- [Learning Robust Representations via Multi-View Information Bottleneck](https://mlsanity.com/paper/2002.07017/) (2020)
- [A Study on Overfitting in Deep Reinforcement Learning](https://mlsanity.com/paper/1804.06893/) (2018)
- [Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning](https://mlsanity.com/paper/2101.05265/) (2021)
- [Generalization and Regularization in DQN](https://mlsanity.com/paper/1810.00123/) (2018)
- [A Survey of Multi-View Representation Learning](https://mlsanity.com/paper/1610.01206/) (2016)
- [Decoupling Value and Policy for Generalization in Reinforcement Learning](https://mlsanity.com/paper/2102.10330/) (2021)
- [Deep Reinforcement and InfoMax Learning](https://mlsanity.com/paper/2006.07217/) (2020)
- [The Conditional Entropy Bottleneck](https://mlsanity.com/paper/2002.05379/) (2020)

## Built on by (most cited first, 30 of 31)

- [A Survey of Zero-shot Generalisation in Deep Reinforcement Learning](https://mlsanity.com/paper/2111.09794/) (2021)
- [PLASTIC: Improving Input and Label Plasticity for Sample Efficient Reinforcement Learning](https://mlsanity.com/paper/2306.10711/) (2023)
- [Efficient Knowledge Distillation from Model Checkpoints](https://mlsanity.com/paper/2210.06458/) (2022)
- [Look where you look! Saliency-guided Q-networks for generalization in visual Reinforcement Learning](https://mlsanity.com/paper/2209.09203/) (2022)
- [On the Importance of Exploration for Generalization in Reinforcement Learning](https://mlsanity.com/paper/2306.05483/) (2023)
- [Revisiting Plasticity in Visual Reinforcement Learning: Data, Modules and Training Stages](https://mlsanity.com/paper/2310.07418/) (2023)
- [Behavior Contrastive Learning for Unsupervised Skill Discovery](https://mlsanity.com/paper/2305.04477/) (2023)
- [The Generalization Gap in Offline Reinforcement Learning](https://mlsanity.com/paper/2312.05742/) (2023)
- [A Comprehensive Survey of Data Augmentation in Visual Reinforcement Learning](https://mlsanity.com/paper/2210.04561/) (2022)
- [Learning Representations for Pixel-based Control: What Matters and Why?](https://mlsanity.com/paper/2111.07775/) (2021)
- [Selective Visual Representations Improve Convergence and Generalization for Embodied AI](https://mlsanity.com/paper/2311.04193/) (2023)
- [A Survey of State Representation Learning for Deep Reinforcement Learning](https://mlsanity.com/paper/2506.17518/) (2025)
- [SimSR: Simple Distance-based State Representation for Deep Reinforcement Learning](https://mlsanity.com/paper/2112.15303/) (2021)
- [Normalization Enhances Generalization in Visual Reinforcement Learning](https://mlsanity.com/paper/2306.00656/) (2023)
- [Reinforcement Learning with Generalizable Gaussian Splatting](https://mlsanity.com/paper/2404.07950/) (2024)
- [Bridging the Sim-to-Real Gap from the Information Bottleneck Perspective](https://mlsanity.com/paper/2305.18464/) (2023)
- [Learning Better with Less: Effective Augmentation for Sample-Efficient Visual Reinforcement Learning](https://mlsanity.com/paper/2305.16379/) (2023)
- [Learning to Discover Skills through Guidance](https://mlsanity.com/paper/2310.20178/) (2023)
- [Generalization in Visual Reinforcement Learning with the Reward Sequence Distribution](https://mlsanity.com/paper/2302.09601/) (2023)
- [Multimodal Information Bottleneck for Deep Reinforcement Learning with Multiple Sensors](https://mlsanity.com/paper/2410.17551/) (2024)
- [Robust Multi-agent Communication via Multi-view Message Certification](https://mlsanity.com/paper/2305.13936/) (2023)
- [Sequential Action-Induced Invariant Representation for Reinforcement Learning](https://mlsanity.com/paper/2309.12628/) (2023)
- [Tackling Visual Control via Multi-View Exploration Maximization](https://mlsanity.com/paper/2211.15233/) (2022)
- [Efficient Training of Generalizable Visuomotor Policies via Control-Aware Augmentation](https://mlsanity.com/paper/2401.09258/) (2024)
- [SeMOPO: Learning High-quality Model and Policy from Low-quality Offline Visual Datasets](https://mlsanity.com/paper/2406.09486/) (2024)
- [The Synergy of LLMs & RL Unlocks Offline Learning of Generalizable Language-Conditioned Policies with Low-fidelity Data](https://mlsanity.com/paper/2412.06877/) (2024)
- [Topology Enhanced MARL for Multi-Agent Cooperative Decision-Making of CAVs](https://mlsanity.com/paper/2507.12110/) (2025)
- [Rollout Total Correlation for Deep Reinforcement Learning](https://mlsanity.com/paper/2209.05333/) (2022)
- [Robust Offline Reinforcement Learning -- Certify the Confidence Interval](https://mlsanity.com/paper/2309.16631/) (2023)
- [Sparse Masked Attention Policies for Reliable Generalization](https://mlsanity.com/paper/2602.19956/) (2026)

## Abstract (arXiv)

Deep reinforcement learning (DRL) agents are often sensitive to visual changes that were unseen in their training environments. To address this problem, we leverage the sequential nature of RL to learn robust representations that encode only task-relevant information from observations based on the unsupervised multi-view setting. Specifically, we introduce a novel contrastive version of the Multi-View Information Bottleneck (MIB) objective for temporal data. We train RL agents from pixels with this auxiliary objective to learn robust representations that can compress away task-irrelevant information and are predictive of task-relevant dynamics. This approach enables us to train high-performance policies that are robust to visual distractions and can generalize well to unseen environments. We demonstrate that our approach can achieve SOTA performance on a diverse set of visual control tasks in the DeepMind Control Suite when the background is replaced with natural videos. In addition, we show that our approach outperforms well-established baselines for generalization to unseen environments on the Procgen benchmark. Our code is open-sourced and available at https://github. com/BU-DEPEND-Lab/DRIBO.