# COLREG-Compliant Collision Avoidance for Unmanned Surface Vehicle using Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2006.09540 · HTML: https://mlsanity.com/paper/2006.09540/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2020.

## Built on by (most cited first, 17 of 17)

- [Hybrid analysis and modeling, eclecticism, and multifidelity computing toward digital twin revolution](https://mlsanity.com/paper/2103.14629/) (2021)
- [Risk-based implementation of COLREGs for autonomous surface vehicles using deep reinforcement learning](https://mlsanity.com/paper/2112.00115/) (2021)
- [Explaining a Deep Reinforcement Learning Docking Agent Using Linear Model Trees with User Adapted Visualization](https://mlsanity.com/paper/2203.00368/) (2022)
- [2-Level Reinforcement Learning for Ships on Inland Waterways: Path Planning and Following](https://mlsanity.com/paper/2307.16769/) (2023)
- [Provable Traffic Rule Compliance in Safe Reinforcement Learning on the Open Sea](https://mlsanity.com/paper/2402.08502/) (2024)
- [Distributional Reinforcement Learning based Integrated Decision Making and Control for Autonomous Surface Vehicles](https://mlsanity.com/paper/2412.09466/) (2024)
- [Stranding Risk for Underactuated Vessels in Complex Ocean Currents: Analysis and Controllers](https://mlsanity.com/paper/2307.01917/) (2023)
- [Modular Control Architecture for Safe Marine Navigation: Reinforcement Learning and Predictive Safety Filters](https://mlsanity.com/paper/2312.01855/) (2023)
- [Depth-Constrained ASV Navigation with Deep RL and Limited Sensing](https://mlsanity.com/paper/2504.18253/) (2025)
- [Approximating a deep reinforcement learning docking agent using linear model trees](https://mlsanity.com/paper/2203.00369/) (2022)
- [Aeolus Ocean -- A simulation environment for the autonomous COLREG-compliant navigation of Unmanned Surface Vehicles using Deep Reinforcement Learning and Maritime Object Detection](https://mlsanity.com/paper/2307.06688/) (2023)
- [Variational Autoencoders for exteroceptive perception in reinforcement learning-based collision avoidance](https://mlsanity.com/paper/2404.00623/) (2024)
- [Guidance Design for Escape Flight Vehicle Using Evolution Strategy Enhanced Deep Reinforcement Learning](https://mlsanity.com/paper/2405.03711/) (2024)
- [Falsification-driven reinforcement learning for maritime motion planning](https://mlsanity.com/paper/2510.06970/) (2025)
- [Sim2Sea: Sim-to-Real Policy Transfer for Maritime Vessel Navigation in Congested Waters](https://mlsanity.com/paper/2603.04057/) (2026)
- [Cross-Platform Control for Autonomous Surface Vehicles via Adaptive Reinforcement Learning](https://mlsanity.com/paper/2607.02037/) (2026)
- [A Large-Scale AIS Dataset from Finnish Water](https://mlsanity.com/paper/2609.12938/) (2026)

## Abstract (arXiv)

Path Following and Collision Avoidance, be it for unmanned surface vessels or other autonomous vehicles, are two fundamental guidance problems in robotics. For many decades, they have been subject to academic study, leading to a vast number of proposed approaches. However, they have mostly been treated as separate problems, and have typically relied on non-linear first-principles models with parameters that can only be determined experimentally. The rise of Deep Reinforcement Learning (DRL) in recent years suggests an alternative approach: end-to-end learning of the optimal guidance policy from scratch by means of a trial-and-error based approach. In this article, we explore the potential of Proximal Policy Optimization (PPO), a DRL algorithm with demonstrated state-of-the-art performance on Continuous Control tasks, when applied to the dual-objective problem of controlling an underactuated Autonomous Surface Vehicle in a COLREGs compliant manner such that it follows an a priori known desired path while avoiding collisions with other vessels along the way. Based on high-fidelity elevation and AIS tracking data from the Trondheim Fjord, an inlet of the Norwegian sea, we evaluate the trained agent's performance in challenging, dynamic real-world scenarios where the ultimate success of the agent rests upon its ability to navigate non-uniform marine terrain while handling challenging, but realistic vessel encounters.