Summary of Long and Short-term Constraints Driven Safe Reinforcement Learning For Autonomous Driving, by Xuemin Hu et al.
Long and Short-Term Constraints Driven Safe Reinforcement Learning for Autonomous Driving
by Xuemin Hu, Pan Chen, Yijun Wen, Bo Tang, Long Chen
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 proposes a novel algorithm for safe reinforcement learning (RL) in autonomous driving systems. The challenge in traditional RL is the high risk of accidents during training due to environmental interactions. Existing safe RL methods, while improving safety, still suffer from a high probability of unsafe states and poor performance optimization. To address this issue, the authors introduce the Long-term and Short-term Constraints (LSTC) algorithm, which combines short-term state safety exploration with long-term overall vehicle safety considerations. The proposed method uses Lagrange multipliers for dual-constraint optimization, achieving higher safety in continuous state-action tasks and better exploration performance in long-distance decision-making tasks compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make self-driving cars safer by developing a new way to teach them how to learn. Right now, teaching self-driving cars is tricky because they have to interact with the environment, which can be dangerous if not done correctly. Existing methods are better than before, but still have some big problems like being too reckless or not doing well enough in certain situations. The authors come up with a new approach called Long and Short-term Constraints (LSTC) that looks at both short-term and long-term safety considerations to make the training process safer and more effective. This means self-driving cars can learn to be safer and explore better, making them more reliable for things like autonomous driving. |
Keywords
* Artificial intelligence * Optimization * Probability * Reinforcement learning
