# Interaction-Aware Multi-Agent Reinforcement Learning for Mobile Agents with Individual Goals

arXiv: https://arxiv.org/abs/1909.12925 · HTML: https://mlsanity.com/paper/1909.12925/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2019.

## Built on by (most cited first, 3 of 3)

- [A Survey of Deep RL and IL for Autonomous Driving Policy Learning](https://mlsanity.com/paper/2101.01993/) (2021)
- [Recursive Reasoning Graph for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2203.02844/) (2022)
- [Learning-based Multi-agent Race Strategies in Formula 1](https://mlsanity.com/paper/2602.23056/) (2026)

## Abstract (arXiv)

In a multi-agent setting, the optimal policy of a single agent is largely dependent on the behavior of other agents. We investigate the problem of multi-agent reinforcement learning, focusing on decentralized learning in non-stationary domains for mobile robot navigation. We identify a cause for the difficulty in training non-stationary policies: mutual adaptation to sub-optimal behaviors, and we use this to motivate a curriculum-based strategy for learning interactive policies. The curriculum has two stages. First, the agent leverages policy gradient algorithms to learn a policy that is capable of achieving multiple goals. Second, the agent learns a modifier policy to learn how to interact with other agents in a multi-agent setting. We evaluated our approach on both an autonomous driving lane-change domain and a robot navigation domain.