Summary of Using Deep Reinforcement Learning to Promote Sustainable Human Behaviour on a Common Pool Resource Problem, by Raphael Koster et al.
Using deep reinforcement learning to promote sustainable human behaviour on a common pool resource problem
by Raphael Koster, Miruna Pîslar, Andrea Tacchetti, Jan Balaguer, Leqi Liu, Romuald Elie, Oliver P. Hauser, Karl Tuyls, Matt Botvinick, Christopher Summerfield
First submitted to arxiv on: 23 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computers and Society (cs.CY); Computer Science and Game Theory (cs.GT)
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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 In this paper, researchers employ deep reinforcement learning (RL) to design a resource allocation mechanism that fosters cooperative behavior in social dilemmas. By training neural networks to mimic human participants and then using RL to train a social planner, the authors demonstrate how to create a system that promotes sustainable contributions to a common pool resource. The study finds that an agent’s generosity is crucial in achieving high surplus levels, as it increases human returns over baseline mechanisms. Furthermore, this RL approach allows for the development of explainable policies that are more popular among participants. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists use artificial intelligence to find ways to get people to work together and share resources fairly. They created a computer game where players made decisions about how to divide up rewards, and then used an AI system called deep reinforcement learning to figure out the best way to do this. The researchers found that when the AI was nice and generous, it helped everyone make more money than they would have otherwise. This approach also led to creating rules that people liked and understood better. |
Keywords
» Artificial intelligence » Reinforcement learning
