Multi-Agent Systems

Published:

A multi-agent system is made up of several autonomous agents that operate in the same setting and affect one another’s results. Each agent may have its own goals, and their interactions can lead to cooperation, competition, or a mix of both. Examples include trading bots in financial markets or characters learning strategies in multiplayer games.

Learning in a multi-agent environment is more challenging than in single-agent setups because the behavior of the environment keeps changing as every agent updates its strategy. To succeed, methods must address shifting dynamics and situations where agents negotiate or coordinate. Research in this area explores ways to train agents together while letting them act independently during execution, as well as techniques for modeling opponents and encouraging stable group behavior.

Follow us on Facebook and LinkedIn to keep abreast of our latest news and articles