MARL-Coordination-Risk-Dataset-India-Relevant-Tasks

This dataset simulates the self-reinforcing non-stationarity problem in multi-agent RL: from any one agent's perspective the environment keeps changing because every other agent is also learning. The simulation spans 1575 configurations across five India-relevant tasks, seven coordination methods spanning a naive independent-learners baseline through six methods with published credit-assignment or centralized-training mechanisms, and agent counts from 2 to 32. Twelve grounding sources are documented in marl_theory.py and the README Research basis, including QMIX, MAPPO, MAAC, and the AlphaStar Nature paper.