This is a framework for the research on multi-agent reinforcement learning and the implementation of the experiments in the paper titled by ''Shapley Q-value: A Local Reward Approach to Solve Global Reward Games''.

Deterministic hex-grid soccer environment with two adversarial agents. Implements Q-Learning, Minimax-Q (via LP), and Belief-Q with online belief updates; trains in SE2G/SE6G to reduce state space and evaluates behaviors in the full environment with comprehensive visualizations.

Project 3 of Udacity's Deep Reinforcement Learning Nanodegree Program