# Active Legibility in Multiagent Reinforcement Learning

arXiv: https://arxiv.org/abs/2410.20954 · HTML: https://mlsanity.com/paper/2410.20954/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 65 references that are in the map.
- First posted on arXiv 28 Oct 2024.

## Builds on (most cited first, 24 of 24)

- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Counterfactual Multi-Agent Policy Gradients](https://mlsanity.com/paper/1705.08926/) (2017)
- [The StarCraft Multi-Agent Challenge](https://mlsanity.com/paper/1902.04043/) (2019)
- [Learning to Communicate with Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1605.06676/) (2016)
- [Value-Decomposition Networks For Cooperative Multi-Agent Learning](https://mlsanity.com/paper/1706.05296/) (2017)
- [QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1905.05408/) (2019)
- [Learning Multiagent Communication with Backpropagation](https://mlsanity.com/paper/1605.07736/) (2016)
- [Emergence of Grounded Compositional Language in Multi-Agent Populations](https://mlsanity.com/paper/1703.04908/) (2017)
- [Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2006.10800/) (2020)
- [MAVEN: Multi-Agent Variational Exploration](https://mlsanity.com/paper/1910.07483/) (2019)
- [Autonomous Agents Modelling Other Agents: A Comprehensive Survey and Open Problems](https://mlsanity.com/paper/1709.08071/) (2017)
- [Opponent Modeling in Deep Reinforcement Learning](https://mlsanity.com/paper/1609.05559/) (2016)
- [Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games](https://mlsanity.com/paper/1703.10069/) (2017)
- [Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1901.09207/) (2019)
- [Learning Efficient Multi-agent Communication: An Information Bottleneck Approach](https://mlsanity.com/paper/1911.06992/) (2019)
- [Reinforcement Learning in Economics and Finance](https://mlsanity.com/paper/2003.10014/) (2020)
- [Modelling Bounded Rationality in Multi-Agent Interactions by Generalized Recursive Reasoning](https://mlsanity.com/paper/1901.09216/) (2019)
- [Monte Carlo Sampling Methods for Approximating Interactive POMDPs](https://mlsanity.com/paper/1401.3455/) (2014)
- [A Regularized Opponent Model with Maximum Entropy Objective](https://mlsanity.com/paper/1905.08087/) (2019)
- [Bayesian Policy Reuse](https://mlsanity.com/paper/1505.00284/) (2015)
- [Policy Regularization for Legible Behavior](https://mlsanity.com/paper/2203.04303/) (2022)
- ["Guess what I'm doing": Extending legibility to sequential decision tasks](https://mlsanity.com/paper/2209.09141/) (2022)
- [Intention Recognition for Multiple Agents](https://mlsanity.com/paper/2112.02513/) (2021)

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

- ["Teammates, Am I Clear?": Analysing Legible Behaviours in Teams](https://mlsanity.com/paper/2507.21631/) (2025)

## Abstract (arXiv)

A multiagent sequential decision problem has been seen in many critical applications including urban transportation, autonomous driving cars, military operations, etc. Its widely known solution, namely multiagent reinforcement learning, has evolved tremendously in recent years. Among them, the solution paradigm of modeling other agents attracts our interest, which is different from traditional value decomposition or communication mechanisms. It enables agents to understand and anticipate others' behaviors and facilitates their collaboration. Inspired by recent research on the legibility that allows agents to reveal their intentions through their behavior, we propose a multiagent active legibility framework to improve their performance. The legibility-oriented framework allows agents to conduct legible actions so as to help others optimise their behaviors. In addition, we design a series of problem domains that emulate a common scenario and best characterize the legibility in multiagent reinforcement learning. The experimental results demonstrate that the new framework is more efficient and costs less training time compared to several multiagent reinforcement learning algorithms.