Summary of Contractual Reinforcement Learning: Pulling Arms with Invisible Hands, by Jibang Wu et al.
Contractual Reinforcement Learning: Pulling Arms with Invisible Hands
by Jibang Wu, Siyu Chen, Mengdi Wang, Huazheng Wang, Haifeng Xu
First submitted to arxiv on: 1 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT); Theoretical Economics (econ.TH)
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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 proposed theoretical framework, contractual reinforcement learning, aims to align economic interests of stakeholders in online learning problems through contract design. The framework emerges from Markov decision processes, where a learning principal seeks to influence an agent’s action policy for common interests via payment rules. To solve the planning problem, an efficient dynamic programming algorithm is designed, while for the learning problem, no-regret algorithms are introduced to balance exploration and exploitation. The results include tailored search algorithms that achieve () regret for several natural classes of problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem in machine learning. Right now, people can’t control how data is collected or what content is created online. This paper proposes a way to fix this by creating contracts that help different groups work together and make good decisions. They use a special type of math called Markov decision processes to figure out the best way to design these contracts. The results show that their method works really well, with some algorithms achieving great results in just a few tries. |
Keywords
» Artificial intelligence » Machine learning » Online learning » Reinforcement learning
