Scarce-Grid

Multi-agent RL environment for scarce reward collection

Tech Stack

About This Project

A multi-agent reinforcement learning environment where agents compete and cooperate to collect scarce rewards on a grid world. Built on Gymnasium with support for tabular Q-Learning, Deep Q-Networks (DQN), game-theoretic analysis, and reward shaping experiments.