"Teammates, Am I Clear?": Analysing Legible Behaviours in Teams
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Abstract (arXiv)
In this paper we investigate the notion of legibility in sequential decision-making in the context of teams and teamwork. There have been works that extend the notion of legibility to sequential decision making, for deterministic and for stochastic scenarios. However, these works focus on one agent interacting with one human, foregoing the benefits of having legible decision making in teams of agents or in team configurations with humans. In this work we propose an extension of legible decision-making to multi-agent settings that improves the performance of agents working in collaboration. We showcase the performance of legible decision making in team scenarios using our proposed extension in multi-agent benchmark scenarios. We show that a team with a legible agent is able to outperform a team composed solely of agents with standard optimal behaviour.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 28 references that are in the map. First posted on arXiv 29 Jul 2025.
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References · 9
- 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
- 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 785
- 2017Value-Decomposition Networks For Cooperative Multi-Agent Learningcited by 503
- 2020Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Taskscited by 212
- 2020Shared Experience Actor-Critic for Multi-Agent Reinforcement Learningcited by 116
- 2018A Unified Framework for Planning in Adversarial and Cooperative Environmentscited by 15
- 2022Encouraging Human Interaction with Robot Teams: Legible and Fair Subtask Allocationscited by 3
- 2022"Guess what I'm doing": Extending legibility to sequential decision taskscited by 2
- 2024Active Legibility in Multiagent Reinforcement Learningcited by 1
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