Evaluating Robustness of Reinforcement Learning Algorithms for Autonomous Shipping
On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control
Abstract (arXiv)
Recently, there has been growing interest in autonomous shipping due to its potential to improve maritime efficiency and safety. The use of advanced technologies, such as artificial intelligence, can address the current navigational and operational challenges in autonomous shipping. In particular, inland waterway transport (IWT) presents a unique set of challenges, such as crowded waterways and variable environmental conditions. In such dynamic settings, the reliability and robustness of autonomous shipping solutions are critical factors for ensuring safe operations. This paper examines the robustness of benchmark deep reinforcement learning (RL) algorithms, implemented for IWT within an autonomous shipping simulator, and their ability to generate effective motion planning policies. We demonstrate that a model-free approach can achieve an adequate policy in the simulator, successfully navigating port environments never encountered during training. We focus particularly on Soft-Actor Critic (SAC), which we show to be inherently more robust to environmental disturbances compared to MuZero, a state-of-the-art model-based RL algorithm. In this paper, we take a significant step towards developing robust, applied RL frameworks that can be generalized to various vessel types and navigate complex port- and inland environments and scenarios.
Key facts. Cited by 1 paper 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 32 references that are in the map. First posted on arXiv 7 Nov 2024.
Builds on
15 of 32 references.
References · 15
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3 more
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Citing papers per year: 2025: 1.
1 paper.
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- 2026Realistic Curriculum Reinforcement Learning for Autonomous and Sustainable Marine Vessel Navigation
- 2024Aquatic Navigation: A Challenging Benchmark for Deep Reinforcement Learning
- 2026SMaRT-Tug: Structured Multi-Agent Reinforcement Learning for Physics-Based Tugboat-Barge Collaborative Manipulation ↗
- 2025Evaluating Robustness of Deep Reinforcement Learning for Autonomous Surface Vehicle Control in Field Tests
- 2024A Deep Reinforcement Learning Framework and Methodology for Reducing the Sim-to-Real Gap in ASV Navigation