Entropy Based Independent Learning in Anonymous Multi-Agent Settings

Authors

  • Tanvi Verma Singapore Management University
  • Pradeep Varakantham Singapore Management University
  • Hoong Chuin Lau Singapore Management University

Abstract

Efficient sequential matching of supply and demand is a problem of interest in many online to offline services. For instance, Uber, Lyft, Grab for matching taxis to customers; Ubereats, Deliveroo, FoodPanda etc for matching restaurants to customers. In these online to offline service problems, individuals who are responsible for supply (e.g., taxi drivers, delivery bikes or delivery van drivers) earn more by being at the ”right” place at the ”right” time. We are interested in developing approaches that learn to guide individuals to be in the ”right” place at the ”right” time (to maximize revenue) in the presence of other similar ”learning” individuals and only local aggregated observation of other agents states (e.g., only number of other taxis in same zone as current agent).

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Published

2021-05-25

How to Cite

Verma, T., Varakantham, P., & Lau, H. C. (2021). Entropy Based Independent Learning in Anonymous Multi-Agent Settings. Proceedings of the International Conference on Automated Planning and Scheduling, 29(1), 655-663. Retrieved from https://ojs.aaai.org/index.php/ICAPS/article/view/3533