Decentralised Learning in Systems with Many, Many Strategic Agents

Understand

Although multi-agent reinforcement learning can tackle systems of strategically interacting entities, it currently fails in scalability and lacks rigorous convergence guarantees.

• Crucially, learning in multi-agent systems can become intractable due to the explosion in the size of the state-action space as the number of agents increases.
• In this paper, we propose a method for computing closed-loop optimal policies in multi-agent systems that scales independently of the number of agents.
• This allows us to show, for the first time, successful convergence to optimal behaviour in systems with an unbounded number of interacting adaptive learners.