# Information-Bottleneck-Based Behavior Representation Learning for Multi-agent Reinforcement learning

arXiv: https://arxiv.org/abs/2109.14188 · HTML: https://mlsanity.com/paper/2109.14188/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 16 references that are in the map.
- First posted on arXiv 29 Sep 2021.

## Builds on (most cited first, 9 of 9)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Deep Learning and the Information Bottleneck Principle](https://mlsanity.com/paper/1503.02406/) (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)
- [Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1702.08887/) (2017)
- [Opponent Modeling in Deep Reinforcement Learning](https://mlsanity.com/paper/1609.05559/) (2016)
- [Combined Reinforcement Learning via Abstract Representations](https://mlsanity.com/paper/1809.04506/) (2018)
- [Learning Task-Driven Control Policies via Information Bottlenecks](https://mlsanity.com/paper/2002.01428/) (2020)

## Built on by (most cited first, 2 of 2)

- [A Survey of State Representation Learning for Deep Reinforcement Learning](https://mlsanity.com/paper/2506.17518/) (2025)
- [Learning to Advise and Learning from Advice in Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2205.11163/) (2022)

## Abstract (arXiv)

In multi-agent deep reinforcement learning, extracting sufficient and compact information of other agents is critical to attain efficient convergence and scalability of an algorithm. In canonical frameworks, distilling of such information is often done in an implicit and uninterpretable manner, or explicitly with cost functions not able to reflect the relationship between information compression and utility in representation. In this paper, we present Information-Bottleneck-based Other agents' behavior Representation learning for Multi-agent reinforcement learning (IBORM) to explicitly seek low-dimensional mapping encoder through which a compact and informative representation relevant to other agents' behaviors is established. IBORM leverages the information bottleneck principle to compress observation information, while retaining sufficient information relevant to other agents' behaviors used for cooperation decision. Empirical results have demonstrated that IBORM delivers the fastest convergence rate and the best performance of the learned policies, as compared with implicit behavior representation learning and explicit behavior representation learning without explicitly considering information compression and utility.