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Offsetting Unequal Competition through RL-assisted Incentive Schemes. (arXiv:2201.01450v1 [cs.GT])
Jan. 6, 2022, 2:10 a.m. | Paramita Koley, Aurghya Maiti, Sourangshu Bhattacharya, Niloy Ganguly
cs.LG updates on arXiv.org arxiv.org
This paper investigates the dynamics of competition among organizations with
unequal expertise. Multi-agent reinforcement learning has been used to simulate
and understand the impact of various incentive schemes designed to offset such
inequality. We design Touch-Mark, a game based on well-known
multi-agent-particle-environment, where two teams (weak, strong) with unequal
but changing skill levels compete against each other. For training such a game,
we propose a novel controller assisted multi-agent reinforcement learning
algorithm \our\, which empowers each agent with an ensemble …
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