There are 2 repositories under zero-sum-games topic.
A custom MARL (multi-agent reinforcement learning) environment where multiple agents trade against one another (self-play) in a zero-sum continuous double auction. Ray [RLlib] is used for training.
Implementing different learning algorithms and analyzing their performance in a Markov game model called the Soccer Game
This repository analyses Strategic form games for N-player calculating various Equilibrium's, Calculate MSNE for 2-Player strategic form and zero sum game, Also contains algorithm for N-player finite Mechanism design to check if social choice function is SDSE, Ex-Post-efficient and Non-dictatorial.
A simple Nash Equilibrium solver for two-player zero-sum games
Deep Reinforcement Learning algorithms to play Connect4 using a combination of Supervised Learning and Reinforcement Learning
A basic Othello (Reversi) implementation that is playable by both humans and general minimax alpha-beta pruning agents
University Class AI Project using Minimax Algorithm and Heuristics to make an AI that can play the game
Equilibria computation in zero-sum games.
Some applications of optimization using linear, binary, and integer programming.
A Python package for game theory, especially its visualization.