# Decentralized Non-communicating Multiagent Collision Avoidance with Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1609.07845 · HTML: https://mlsanity.com/paper/1609.07845/

## Key facts

- Cited by 118 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2016.

## Built on by (most cited first, 30 of 118)

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- [Where to go next: Learning a Subgoal Recommendation Policy for Navigation Among Pedestrians](https://mlsanity.com/paper/2102.13073/) (2021)
- [Safe Reinforcement Learning Using Black-Box Reachability Analysis](https://mlsanity.com/paper/2204.07417/) (2022)
- [NavRL: Learning Safe Flight in Dynamic Environments](https://mlsanity.com/paper/2409.15634/) (2024)
- [VI-IKD: High-Speed Accurate Off-Road Navigation using Learned Visual-Inertial Inverse Kinodynamics](https://mlsanity.com/paper/2203.15983/) (2022)
- [Multi-Agent Dynamic Relational Reasoning for Social Robot Navigation](https://mlsanity.com/paper/2401.12275/) (2024)
- [Deep Reinforcement Learning-based Obstacle Avoidance for Robot Movement in Warehouse Environments](https://mlsanity.com/paper/2409.14972/) (2024)
- [Conflict Avoidance in Social Navigation -- a Survey](https://mlsanity.com/paper/2106.12113/) (2021)
- [Learning Interaction-Aware Trajectory Predictions for Decentralized Multi-Robot Motion Planning in Dynamic Environments](https://mlsanity.com/paper/2102.05382/) (2021)
- [High-Speed Accurate Robot Control using Learned Forward Kinodynamics and Non-linear Least Squares Optimization](https://mlsanity.com/paper/2206.08487/) (2022)
- [NeuPAN: Direct Point Robot Navigation with End-to-End Model-based Learning](https://mlsanity.com/paper/2403.06828/) (2024)
- [Intention-aware Denoising Diffusion Model for Trajectory Prediction](https://mlsanity.com/paper/2403.09190/) (2024)
- [Occlusion-Aware Crowd Navigation Using People as Sensors](https://mlsanity.com/paper/2210.00552/) (2022)
- [SoNIC: Safe Social Navigation with Adaptive Conformal Inference and Constrained Reinforcement Learning](https://mlsanity.com/paper/2407.17460/) (2024)
- [Language-Conditioned Offline RL for Multi-Robot Navigation](https://mlsanity.com/paper/2407.20164/) (2024)
- [DR-MPC: Deep Residual Model Predictive Control for Real-world Social Navigation](https://mlsanity.com/paper/2410.10646/) (2024)
- [HEIGHT: Heterogeneous Interaction Graph Transformer for Robot Navigation in Crowded and Constrained Environments](https://mlsanity.com/paper/2411.12150/) (2024)
- [Collision Avoidance and Navigation for a Quadrotor Swarm Using End-to-end Deep Reinforcement Learning](https://mlsanity.com/paper/2309.13285/) (2023)
- [SELFI: Autonomous Self-Improvement with Reinforcement Learning for Social Navigation](https://mlsanity.com/paper/2403.00991/) (2024)
- [Discrete GCBF Proximal Policy Optimization for Multi-agent Safe Optimal Control](https://mlsanity.com/paper/2502.03640/) (2025)
- [Human-Inspired Multi-Agent Navigation using Knowledge Distillation](https://mlsanity.com/paper/2103.10000/) (2021)
- [Robot Navigation in a Crowd by Integrating Deep Reinforcement Learning and Online Planning](https://mlsanity.com/paper/2102.13265/) (2021)
- [Connecting Deep-Reinforcement-Learning-based Obstacle Avoidance with Conventional Global Planners using Waypoint Generators](https://mlsanity.com/paper/2104.03663/) (2021)
- [Exploiting Proximity-Aware Tasks for Embodied Social Navigation](https://mlsanity.com/paper/2212.00767/) (2022)

## 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.