Summary of Evaluating Robustness Of Reinforcement Learning Algorithms For Autonomous Shipping, by Bavo Lesy et al.
Evaluating Robustness of Reinforcement Learning Algorithms for Autonomous Shipping
by Bavo Lesy, Ali Anwar, Siegfried Mercelis
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A recent surge in interest for autonomous shipping has sparked innovation, aiming to enhance maritime efficiency and safety. Advanced technologies like AI are being harnessed to tackle navigational and operational challenges in this space. Specifically, inland waterway transport (IWT) presents a unique set of hurdles, such as crowded waterways and variable environmental conditions. For reliable and robust autonomous shipping solutions, these factors are critical for ensuring safe operations. This study evaluates the robustness of benchmark deep reinforcement learning (RL) algorithms implemented for IWT within an autonomous shipping simulator, examining their capacity to generate effective motion planning policies. The results demonstrate a model-free approach can achieve an adequate policy in the simulator, successfully navigating port environments never encountered during training. The SAC algorithm is shown to be inherently more robust to environmental disturbances compared to MuZero, a state-of-the-art model-based RL algorithm. This work takes 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. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Autonomous shipping could make maritime transportation safer and more efficient. Researchers are using artificial intelligence to tackle challenges in this area. Inland waterway transport is particularly tricky because of crowded waterways and changing environmental conditions. To make autonomous shipping reliable, the algorithms used need to be robust. This study tested different deep reinforcement learning (RL) algorithms for inland waterway transport within a simulator. The results show that a model-free approach can work well in the simulator, even when navigating new environments. SAC is a better algorithm than MuZero because it’s more robust to changes. |
Keywords
* Artificial intelligence * Reinforcement learning
