# Reinforcement Learned Distributed Multi-Robot Navigation with Reciprocal Velocity Obstacle Shaped Rewards

arXiv: https://arxiv.org/abs/2203.10229 · HTML: https://mlsanity.com/paper/2203.10229/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2022.

## Built on by (most cited first, 16 of 16)

- [Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes](https://mlsanity.com/paper/2408.03539/) (2024)
- [Comprehensive Overview of Reward Engineering and Shaping in Advancing Reinforcement Learning Applications](https://mlsanity.com/paper/2408.10215/) (2024)
- [No Regrets: Investigating and Improving Regret Approximations for Curriculum Discovery](https://mlsanity.com/paper/2408.15099/) (2024)
- [NeuPAN: Direct Point Robot Navigation with End-to-End Model-based Learning](https://mlsanity.com/paper/2403.06828/) (2024)
- [Learning Minimally-Violating Continuous Control for Infeasible Linear Temporal Logic Specifications](https://mlsanity.com/paper/2210.01162/) (2022)
- [Decentralized Multi-Robot Navigation for Autonomous Surface Vehicles with Distributional Reinforcement Learning](https://mlsanity.com/paper/2402.11799/) (2024)
- [BEVNav: Robot Autonomous Navigation Via Spatial-Temporal Contrastive Learning in Bird's-Eye View](https://mlsanity.com/paper/2409.01646/) (2024)
- [Sim-and-Real Reinforcement Learning for Manipulation: A Consensus-based Approach](https://mlsanity.com/paper/2302.13423/) (2023)
- [Toward Scalable Multirobot Control: Fast Policy Learning in Distributed MPC](https://mlsanity.com/paper/2412.19669/) (2024)
- [IR-SIM: A Lightweight Declarative Simulator for Navigation Learning and Benchmarking](https://mlsanity.com/paper/2606.08729/) (2026)
- [MeSA-DRL: Memory-Enhanced Deep Reinforcement Learning for Advanced Socially Aware Robot Navigation in Crowded Environments](https://mlsanity.com/paper/2404.05203/) (2024)
- [Multi-Modal Decentralized Reinforcement Learning for Modular Reconfigurable Lunar Robots](https://mlsanity.com/paper/2510.20347/) (2025)
- [ICCO: Learning an Instruction-conditioned Coordinator for Language-guided Task-aligned Multi-robot Control](https://mlsanity.com/paper/2503.12122/) (2025)
- [Lyapunov Stability Learning with Nonlinear Control via Inductive Biases](https://mlsanity.com/paper/2511.01283/) (2025)
- [Distributed Multi Robot Lunar Cargo Transportation via Phase Decomposed Reinforcement Learning](https://mlsanity.com/paper/2607.00160/) (2026)
- [SRL-MPC: Shape-Aware Reinforcement Learned Model Predictive Control](https://mlsanity.com/paper/2608.21175/) (2026)

## Abstract (arXiv)

The challenges to solving the collision avoidance problem lie in adaptively choosing optimal robot velocities in complex scenarios full of interactive obstacles. In this paper, we propose a distributed approach for multi-robot navigation which combines the concept of reciprocal velocity obstacle (RVO) and the scheme of deep reinforcement learning (DRL) to solve the reciprocal collision avoidance problem under limited information. The novelty of this work is threefold: (1) using a set of sequential VO and RVO vectors to represent the interactive environmental states of static and dynamic obstacles, respectively; (2) developing a bidirectional recurrent module based neural network, which maps the states of a varying number of surrounding obstacles to the actions directly; (3) developing a RVO area and expected collision time based reward function to encourage reciprocal collision avoidance behaviors and trade off between collision risk and travel time. The proposed policy is trained through simulated scenarios and updated by the actor-critic based DRL algorithm. We validate the policy in complex environments with various numbers of differential drive robots and obstacles. The experiment results demonstrate that our approach outperforms the state-of-art methods and other learning based approaches in terms of the success rate, travel time, and average speed. Source code of this approach is available at https://github.com/hanruihua/rl_rvo_nav.