Adversarial Learning for Fairness in Multi-Agent Reinforcement Learning: Mitigating Discrimination in Cooperative Environments

Multi-agent reinforcement learning involves the study of how multiple agents interact within a shared environment and with each other to achieve a common goal. In cooperative environments, agents typically work together to maximize a collective reward. However, in such environments, the op- timization of actions can lead to more capable agents overshadowing less capable ones, as they strive to maximize the expected value of their actions using reinforcement learning techniques. The objective of this project is to address this imbalance by ensuring fairness during the training process. Ad- versarial learning techniques offer a promising approach by enforcing learned representations to be insensitive to specific attributes, thereby mitigating disparities and creating a fair representation and decision-making process among cooperative agents.