# Intent-aware Multi-agent Reinforcement Learning

arXiv: https://arxiv.org/abs/1803.02018 · HTML: https://mlsanity.com/paper/1803.02018/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2018.

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

- [A Survey of Deep RL and IL for Autonomous Driving Policy Learning](https://mlsanity.com/paper/2101.01993/) (2021)
- [A Survey on Context-Aware Multi-Agent Systems: Techniques, Challenges and Future Directions](https://mlsanity.com/paper/2402.01968/) (2024)
- [Theory of Mind as Intrinsic Motivation for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2307.01158/) (2023)
- [Co-Evolving LLM Decision and Skill Bank Agents for Long-Horizon Tasks](https://mlsanity.com/paper/2604.20987/) (2026)
- [Spatial Intention Maps for Multi-Agent Mobile Manipulation](https://mlsanity.com/paper/2103.12710/) (2021)
- [Multi-Agent Inverse Reinforcement Learning in Real World Unstructured Pedestrian Crowds](https://mlsanity.com/paper/2405.16439/) (2024)
- [iPLAN: Intent-Aware Planning in Heterogeneous Traffic via Distributed Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2306.06236/) (2023)
- [GIN: Graph-based Interaction-aware Constraint Policy Optimization for Autonomous Driving](https://mlsanity.com/paper/2206.01488/) (2022)
- [MetaMind: General and Cognitive World Models in Multi-Agent Systems by Meta-Theory of Mind](https://mlsanity.com/paper/2603.00808/) (2026)
- [Intent-Aware Autonomous Driving: A Case Study on Highway Merging Scenarios](https://mlsanity.com/paper/2309.13206/) (2023)
- [Improved Reinforcement Learning in Cooperative Multi-agent Environments Using Knowledge Transfer](https://mlsanity.com/paper/2107.09807/) (2021)
- [CASPER: Cognitive Architecture for Social Perception and Engagement in Robots](https://mlsanity.com/paper/2209.01012/) (2022)

## Abstract (arXiv)

This paper proposes an intent-aware multi-agent planning framework as well as a learning algorithm. Under this framework, an agent plans in the goal space to maximize the expected utility. The planning process takes the belief of other agents' intents into consideration. Instead of formulating the learning problem as a partially observable Markov decision process (POMDP), we propose a simple but effective linear function approximation of the utility function. It is based on the observation that for humans, other people's intents will pose an influence on our utility for a goal. The proposed framework has several major advantages: i) it is computationally feasible and guaranteed to converge. ii) It can easily integrate existing intent prediction and low-level planning algorithms. iii) It does not suffer from sparse feedbacks in the action space. We experiment our algorithm in a real-world problem that is non-episodic, and the number of agents and goals can vary over time. Our algorithm is trained in a scene in which aerial robots and humans interact, and tested in a novel scene with a different environment. Experimental results show that our algorithm achieves the best performance and human-like behaviors emerge during the dynamic process.