Summary of Ad4rl: Autonomous Driving Benchmarks For Offline Reinforcement Learning with Value-based Dataset, by Dongsu Lee et al.
AD4RL: Autonomous Driving Benchmarks for Offline Reinforcement Learning with Value-based Dataset
by Dongsu Lee, Chanin Eom, Minhae Kwon
First submitted to arxiv on: 3 Apr 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 The paper addresses limitations in offline reinforcement learning by providing autonomous driving datasets and benchmarks. It offers 19 real-world human driver datasets and seven popular algorithms for three realistic driving scenarios. The research also provides a unified decision-making process model, serving as a reference framework for algorithm design. The goal is to enhance the practicality of offline reinforcement learning through the use of pre-collected large datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps make offline reinforcement learning more practical by providing real-world human driver datasets and benchmarks for autonomous driving. This can help researchers improve existing algorithms for realistic scenarios like traffic jams or construction zones. The goal is to make it easier to develop and test new self-driving car systems. |
Keywords
* Artificial intelligence * Reinforcement learning
