# Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1709.10082 · HTML: https://mlsanity.com/paper/1709.10082/

## Key facts

- Cited by 83 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2017.

## Built on by (most cited first, 30 of 83)

- [Partially Observable Markov Decision Processes in Robotics: A Survey](https://mlsanity.com/paper/2209.10342/) (2022)
- [Principles and Guidelines for Evaluating Social Robot Navigation Algorithms](https://mlsanity.com/paper/2306.16740/) (2023)
- [Intention Aware Robot Crowd Navigation with Attention-Based Interaction Graph](https://mlsanity.com/paper/2203.01821/) (2022)
- [CoNVOI: Context-aware Navigation using Vision Language Models in Outdoor and Indoor Environments](https://mlsanity.com/paper/2403.15637/) (2024)
- [Learning a State Representation and Navigation in Cluttered and Dynamic Environments](https://mlsanity.com/paper/2103.04351/) (2021)
- [Distributed Heuristic Multi-Agent Path Finding with Communication](https://mlsanity.com/paper/2106.11365/) (2021)
- [No Regrets: Investigating and Improving Regret Approximations for Curriculum Discovery](https://mlsanity.com/paper/2408.15099/) (2024)
- [Safe Reinforcement Learning Using Black-Box Reachability Analysis](https://mlsanity.com/paper/2204.07417/) (2022)
- [Model-based Multi-agent Reinforcement Learning: Recent Progress and Prospects](https://mlsanity.com/paper/2203.10603/) (2022)
- [Risk-Conditioned Distributional Soft Actor-Critic for Risk-Sensitive Navigation](https://mlsanity.com/paper/2104.03111/) (2021)
- [Conflict Avoidance in Social Navigation -- a Survey](https://mlsanity.com/paper/2106.12113/) (2021)
- [Centralizing State-Values in Dueling Networks for Multi-Robot Reinforcement Learning Mapless Navigation](https://mlsanity.com/paper/2112.09012/) (2021)
- [Overcoming Exploration: Deep Reinforcement Learning for Continuous Control in Cluttered Environments from Temporal Logic Specifications](https://mlsanity.com/paper/2201.12231/) (2022)
- [A Survey on Reinforcement Learning Methods in Character Animation](https://mlsanity.com/paper/2203.04735/) (2022)
- [Data-heterogeneity-aware Mixing for Decentralized Learning](https://mlsanity.com/paper/2204.06477/) (2022)
- [Graph Neural Networks for Multi-Robot Active Information Acquisition](https://mlsanity.com/paper/2209.12091/) (2022)
- [PTDE: Personalized Training with Distilled Execution for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2210.08872/) (2022)
- [A Vision Based Deep Reinforcement Learning Algorithm for UAV Obstacle Avoidance](https://mlsanity.com/paper/2103.06403/) (2021)
- [Boosting Sample Efficiency and Generalization in Multi-agent Reinforcement Learning via Equivariance](https://mlsanity.com/paper/2410.02581/) (2024)
- [Multi-robot Cooperative Pursuit via Potential Field-Enhanced Reinforcement Learning](https://mlsanity.com/paper/2203.04700/) (2022)
- [Sequential Neural Barriers for Scalable Dynamic Obstacle Avoidance](https://mlsanity.com/paper/2307.03015/) (2023)
- [Efficient Multi-agent Reinforcement Learning by Planning](https://mlsanity.com/paper/2405.11778/) (2024)
- [HEIGHT: Heterogeneous Interaction Graph Transformer for Robot Navigation in Crowded and Constrained Environments](https://mlsanity.com/paper/2411.12150/) (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)
- [Towards Disturbance-Free Visual Mobile Manipulation](https://mlsanity.com/paper/2112.12612/) (2021)
- [Learning Minimally-Violating Continuous Control for Infeasible Linear Temporal Logic Specifications](https://mlsanity.com/paper/2210.01162/) (2022)
- [Learning Team-Based Navigation: A Review of Deep Reinforcement Learning Techniques for Multi-Agent Pathfinding](https://mlsanity.com/paper/2308.05893/) (2023)
- [Locally Interdependent Multi-Agent MDP: Theoretical Framework for Decentralized Agents with Dynamic Dependencies](https://mlsanity.com/paper/2406.06823/) (2024)

## Abstract (arXiv)

Developing a safe and efficient collision avoidance policy for multiple robots is challenging in the decentralized scenarios where each robot generate its paths without observing other robots' states and intents. While other distributed multi-robot collision avoidance systems exist, they often require extracting agent-level features to plan a local collision-free action, which can be computationally prohibitive and not robust. More importantly, in practice the performance of these methods are much lower than their centralized counterparts. We present a decentralized sensor-level collision avoidance policy for multi-robot systems, which directly maps raw sensor measurements to an agent's steering commands in terms of movement velocity. As a first step toward reducing the performance gap between decentralized and centralized methods, we present a multi-scenario multi-stage training framework to find an optimal policy which is trained over a large number of robots on rich, complex environments simultaneously using a policy gradient based reinforcement learning algorithm. We validate the learned sensor-level collision avoidance policy in a variety of simulated scenarios with thorough performance evaluations and show that the final learned policy is able to find time efficient, collision-free paths for a large-scale robot system. We also demonstrate that the learned policy can be well generalized to new scenarios that do not appear in the entire training period, including navigating a heterogeneous group of robots and a large-scale scenario with 100 robots. Videos are available at https://sites.google.com/view/drlmaca