mlsanityData to 14 Oct 2026

Decentralized Multi-Robot Navigation for Autonomous Surface Vehicles with Distributional Reinforcement Learning

arXiv 2402.11799 ↗First posted 19 Feb 2024cs.ROcited by 4

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

Collision avoidance algorithms for Autonomous Surface Vehicles (ASV) that follow the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) have been proposed in recent years. However, it may be difficult and unsafe to follow COLREGs in congested waters, where multiple ASVs are navigating in the presence of static obstacles and strong currents, due to the complex interactions. To address this problem, we propose a decentralized multi-ASV collision avoidance policy based on Distributional Reinforcement Learning, which considers the interactions among ASVs as well as with static obstacles and current flows. We evaluate the performance of the proposed Distributional RL based policy against a traditional RL-based policy and two classical methods, Artificial Potential Fields (APF) and Reciprocal Velocity Obstacles (RVO), in simulation experiments, which show that the proposed policy achieves superior performance in navigation safety, while requiring minimal travel time and energy. A variant of our framework that automatically adapts its risk sensitivity is also demonstrated to improve ASV safety in highly congested environments.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 15 of its 41 references that are in the map. First posted on arXiv 19 Feb 2024.

Builds on

15 of 41 references.

References · 15

  1. 2017A Distributional Perspective on Reinforcement Learningcited by 765
  2. 2018Implicit Quantile Networks for Distributional Reinforcement Learningcited by 369
  3. 2018Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learningcited by 175
  4. 2017Socially Aware Motion Planning with Deep Reinforcement Learningcited by 136
  5. 2016Decentralized Non-communicating Multiagent Collision Avoidance with Deep Reinforcement Learningcited by 118
  6. 2018Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learningcited by 111
  7. 2017Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learningcited by 83
  8. 2019Relational Graph Learning for Crowd Navigationcited by 48
  9. 2022Reinforcement Learned Distributed Multi-Robot Navigation with Reciprocal Velocity Obstacle Shaped Rewardscited by 16
  10. 2021Risk-Conditioned Distributional Soft Actor-Critic for Risk-Sensitive Navigationcited by 15
  11. 2021Minimizing Safety Interference for Safe and Comfortable Automated Driving with Distributional Reinforcement Learningcited by 10
  12. 2023Robust Unmanned Surface Vehicle Navigation with Distributional Reinforcement Learningcited by 8
3 more
  1. 2022Adaptive Risk-Tendency: Nano Drone Navigation in Cluttered Environments with Distributional Reinforcement Learningcited by 6
  2. 2017Multi-Robot Data Gathering Under Buffer Constraints and Intermittent Communicationcited by 2
  3. 2023Robust Route Planning with Distributional Reinforcement Learning in a Stochastic Road Network Environmentcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 1; 2026: 1.

4 papers.

  1. 2024Distributional Reinforcement Learning based Integrated Decision Making and Control for Autonomous Surface Vehiclescited by 2
  2. 2026AsyncShield: A Plug-and-Play Edge Adapter for Asynchronous Cloud-based VLA Navigationcited by 2
  3. 2024MarineFormer: A Spatio-Temporal Attention Model for USV Navigation in Dynamic Marine Environmentscited by 1
  4. 2025TERL: Large-Scale Multi-Target Encirclement Using Transformer-Enhanced Reinforcement Learningcited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2024A Deep Reinforcement Learning Framework and Methodology for Reducing the Sim-to-Real Gap in ASV Navigation
  2. 2026Reinforcement Learning Based Heterogeneous Task Allocation Under Spatio-Temporal Environmental Uncertainty for Multi-Robot Teams
  3. 2025Depth-Constrained ASV Navigation with Deep RL and Limited Sensing
  4. 2025Safe Reinforcement Learning with a Predictive Safety Filter for Motion Planning and Control: A Drifting Vehicle Example
  5. 2024Multiple Ships Cooperative Navigation and Collision Avoidance using Multi-agent Reinforcement Learning with Communication ↗
  6. 2026Sim-to-Real RL for ASVs using SysID
  7. 2025RoboRAN: A Unified Robotics Framework for Reinforcement Learning-Based Autonomous Navigation
  8. 2026Cross-Platform Control for Autonomous Surface Vehicles via Adaptive Reinforcement Learning