Convergence to Nash Equilibrium: A Comparative Study of Rock-Paper-Scissors Algorithms

Rock-paper-scissors is one of the most established imperfect information games in Game theory. The Nash Equilibrium of an RPS game is relatively simple but computationally intractable; hence, various algorithms are employed to converge to a state of maximum payoff. In this paper, five algorithms, na...

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Bibliographic Details
Published in2023 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) pp. 329 - 334
Main Authors Vadali, Geetika, Reddy, M Deekshitha, Rani, Ritu, Bansal, Poonam
Format Conference Proceeding
LanguageEnglish
Published IEEE 03.11.2023
Subjects
Convergence
Deep Reinforcement learning
Game Theory
Games
Imperfect Information Game
Monte Carlo methods
Nash equilibrium
Optimization
Regret
Reinforcement learning
Rock-Paper-Scissors
Visualization
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Summary:Rock-paper-scissors is one of the most established imperfect information games in Game theory. The Nash Equilibrium of an RPS game is relatively simple but computationally intractable; hence, various algorithms are employed to converge to a state of maximum payoff. In this paper, five algorithms, namely - Counterfactual Regret Minimization, Monte Carlo Tree Search, Q-Iearning, Deep Q-Network and Proximal Policy Optimization, have been compared on the evaluation metrics of average reward, draw ratio and convergence speed. Throughout the comparative analysis, visualising the learning curves, and qualitative comparison, Q-Iearning has shown the best convergence to Nash equilibrium for RPS.
DOI:10.1109/ICCCIS60361.2023.10425577