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Iterated Boxed Pigs Game: A Reinforcement Learning Approach

In: Operations Research Proceedings 2022

Author

Listed:
  • Rudy Milani

    (Universität der Bundeswehr München)

  • Maximilian Moll

    (Universität der Bundeswehr München)

  • Stefan Pickl

    (Universität der Bundeswehr München)

Abstract

This paper analyzes the iterated version of the well-known Boxed Pigs game through Reinforcement Learning. In this scenario, there are two differently sized players (pigs) that compete against each other. The core idea is about sacrificing a pay-off in order to generate some rewards. In our iterated version, these pigs play this game repeatedly using different strategies. We carry out two experiments: in the first one, we train two Q-learning agents against each other to see which equilibrium will be generated. In the second one, we pit the Reinforcement Learning agent against a fixed policy pig. The results of this experiment confirm the ability of Reinforcement Learning techniques in finding the best strategy for maximizing the return independently from the other player choices.

Suggested Citation

  • Rudy Milani & Maximilian Moll & Stefan Pickl, 2023. "Iterated Boxed Pigs Game: A Reinforcement Learning Approach," Lecture Notes in Operations Research, in: Oliver Grothe & Stefan Nickel & Steffen Rebennack & Oliver Stein (ed.), Operations Research Proceedings 2022, chapter 0, pages 617-623, Springer.
    • Handle: RePEc:spr:lnopch:978-3-031-24907-5_74
    DOI: 10.1007/978-3-031-24907-5_74
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