Summary of Aquatic Navigation: a Challenging Benchmark For Deep Reinforcement Learning, by Davide Corsi et al.
Aquatic Navigation: A Challenging Benchmark for Deep Reinforcement Learning
by Davide Corsi, Davide Camponogara, Alessandro Farinelli
First submitted to arxiv on: 30 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Robotics (cs.RO)
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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 This paper explores the application of Deep Reinforcement Learning (DRL) to real-world robotic systems, focusing on unpredictable and non-stationary environments. Despite remarkable successes in various scenarios, such environments pose significant challenges to DRL methods, undermining fundamental requirements like Markovian properties. To address this, the authors propose a new benchmarking environment for aquatic navigation, integrating game engines with DRL. They focus on PPO, one of the most widely accepted algorithms, and propose advanced training techniques like curriculum learning and learnable hyperparameters. The paper’s extensive empirical evaluation demonstrates that combining these ingredients can achieve promising results. The simulation environment and training baselines are freely available to facilitate further research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using artificial intelligence (AI) to help robots do things in real-world situations. Right now, AI can already control some robots in certain situations, but there are still many challenges to overcome before it can be used reliably in the real world. The authors propose a new way of testing how well these AI systems work by having them navigate through water. They focus on one type of AI called PPO and come up with new ways of training it so that it can make better decisions. |
Keywords
» Artificial intelligence » Curriculum learning » Reinforcement learning
