Evaluating Robustness of Reinforcement Learning Algorithms for Autonomous Shipping

TL;DR

This paper evaluates the robustness of deep reinforcement learning algorithms, especially Soft-Actor Critic, for autonomous inland waterway shipping, demonstrating their ability to handle environmental disturbances and generalize to new port scenarios.

Contribution

It compares model-free and model-based RL algorithms in autonomous shipping, highlighting the robustness of Soft-Actor Critic in complex, dynamic environments.

Findings

Soft-Actor Critic outperforms MuZero in robustness to disturbances

Model-free RL can navigate unseen port environments effectively

The study advances RL frameworks for diverse vessel navigation scenarios

Abstract

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.