Multi-Agent Reinforcement Learning

They’ve taken this approach with Cicero.

Read Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms

Why is MARL hard?

There are many reasons why MARL is hard, including:

• Non-stationarity of the environment, making theoretical analysis super hard
• Joint action space exponentiates search space in size
• “who knows what”?

But MARL is the future. Autocurricula is a big part of it, to see emergent behavior.

From the DeepMind Paper: Current approaches to artificial intelligence (AI) focus too much on individual agents and not enough on the interactions between agents in a multi-agent system. We should focus on “multi-agent intelligence,” studying how agent interactions lead to the emergence of new behaviours and innovations.

A focus on multi-agent intelligence and autocurricula can lead to the development of more robust, adaptable, and innovative AI systems that can better handle complex, real-world problems.