Decentralized Non-communicating Multiagent Collision Avoidance with Deep Reinforcement Learning
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Abstract (arXiv)
Finding feasible, collision-free paths for multiagent systems can be challenging, particularly in non-communicating scenarios where each agent's intent (e.g. goal) is unobservable to the others. In particular, finding time efficient paths often requires anticipating interaction with neighboring agents, the process of which can be computationally prohibitive. This work presents a decentralized multiagent collision avoidance algorithm based on a novel application of deep reinforcement learning, which effectively offloads the online computation (for predicting interaction patterns) to an offline learning procedure. Specifically, the proposed approach develops a value network that encodes the estimated time to the goal given an agent's joint configuration (positions and velocities) with its neighbors. Use of the value network not only admits efficient (i.e., real-time implementable) queries for finding a collision-free velocity vector, but also considers the uncertainty in the other agents' motion. Simulation results show more than 26 percent improvement in paths quality (i.e., time to reach the goal) when compared with optimal reciprocal collision avoidance (ORCA), a state-of-the-art collision avoidance strategy.
Key facts. Cited by 104 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Sep 2016.
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Citing papers per year: 2021: 23; 2022: 14; 2023: 14; 2024: 27; 2025: 14; 2026: 12.
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- 2026IR-SIM: A Lightweight Declarative Simulator for Navigation Learning and Benchmarking7 Jun 2026
- 2026Enhancing the MADDPG Algorithm for Multi-Agent Learning via Action Inference and Importance Sampling3 Jun 2026
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- 2025Hybrid Motion Planning with Deep Reinforcement Learning for Mobile Robot Navigation31 Dec 2025
- 2025Intrinsic-Motivation Multi-Robot Social Formation Navigation with Coordinated Exploration15 Dec 2025
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