Summary of Sampling-based Safe Reinforcement Learning For Nonlinear Dynamical Systems, by Wesley A. Suttle et al.
Sampling-based Safe Reinforcement Learning for Nonlinear Dynamical Systems
by Wesley A. Suttle, Vipul K. Sharma, Krishna C. Kosaraju, S. Sivaranjani, Ji Liu, Vijay Gupta, Brian M. Sadler
First submitted to arxiv on: 6 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC)
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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 bridges the gap between reinforcement learning (RL) and control theory by developing provably safe and convergent RL algorithms for nonlinear dynamical systems. The authors propose a single-stage approach to hard constraint satisfaction, which learns RL controllers with classical convergence guarantees while satisfying safety constraints throughout training and deployment. This approach outperforms existing benchmarks in simulation, including the control of a quadcopter in an obstacle avoidance problem. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it possible for machines to learn new skills while staying safe. It creates a way for reinforcement learning (RL) algorithms to follow rules that keep them from causing harm. The authors test their approach on a simulated drone and show that it works better than other methods. This is important because it can help us create more reliable and safe AI systems. |
Keywords
* Artificial intelligence * Reinforcement learning
