Imagine a game where multiple players must work together to achieve a common goal. This is similar to what happens in multi-agent systems, but with a twist. Instead of just focusing on the average outcome, these systems consider the whole range of possible outcomes. This approach is called Distributional Reinforcement Learning (RL).

In the past, researchers have combined Distributional RL with multi-agent systems to make them more expressive. However, a fully distributional multi-agent system, where both individual and global value functions are considered, hasn't been fully explored. The challenge lies in ensuring that these systems satisfy a principle called Individual-Global-Max (IGM).

A recent study tackled this issue by proposing a new framework. This framework guarantees that the IGM principle is met. Based on this idea, a practical deep reinforcement learning model called Fully Distributional Multi-Agent Cooperation (FDMAC) was developed.

To test FDMAC, researchers used the StarCraft Multi-Agent Challenge micromanagement environment. The results were impressive. FDMAC outperformed the best baseline by 10. 47% on average in terms of the median test win rate.

This shows that by considering the entire distribution of possible outcomes, multi-agent systems can become even more effective. It's like having a team that doesn't just aim for the average score but also considers how to maximize their chances of winning in various situations.

Exploring the Power of Distributional RL in Multi-Agent Cooperation

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