Summary of Solving Minimum-cost Reach Avoid Using Reinforcement Learning, by Oswin So et al.
Solving Minimum-Cost Reach Avoid using Reinforcement Learning
by Oswin So, Cheng Ge, Chuchu Fan
First submitted to arxiv on: 29 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Robotics (cs.RO); 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 The paper tackles a new optimization problem in reinforcement learning, the minimum cost reach-avoid problem, where the goal is to minimize cumulative costs while reaching a target and avoiding unsafe states. Current methods fail to directly solve this problem due to its unique structure. To address this issue, the authors propose RC-PPO (Reinforcement Learning-based method for solving the minimum-cost reach-avoid problem), which leverages connections to Hamilton-Jacobi reachability. The approach outperforms existing methods on Mujoco simulator benchmarks, achieving up to 57% lower cumulative costs while maintaining goal-reaching rates comparable to state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to get from one place to another without taking a wrong turn. You want to go as efficiently as possible while avoiding danger zones. Right now, computers aren’t very good at solving this problem because they don’t understand the special rules of getting there safely and quickly. The authors of this paper came up with a new way for computers to learn how to solve this challenge using something called Hamilton-Jacobi reachability. They tested their method on some simulation games and found that it worked really well, saving time and energy compared to other methods. |
Keywords
* Artificial intelligence * Optimization * Reinforcement learning
