What does "Multi-agent Learning" mean?
Table of Contents
Multi-agent learning is a field of study where multiple computer programs, or agents, work together to solve problems or complete tasks. These agents can communicate and share information, allowing them to learn from each other and improve their performance over time.
How It Works
In multi-agent learning, each agent can have its own goals and strategies. They learn by trying different actions and seeing the results. If an agent makes a good decision, it will remember that in the future. If it makes a poor choice, it learns not to repeat that action.
Applications
This type of learning is useful in many areas. For example, it can help in managing traffic where many cars communicate with one another to avoid accidents and improve flow. It can also be applied in games, where agents compete or cooperate to achieve a specific goal.
Benefits
By using multi-agent learning, systems can become smarter and more efficient. Agents can adapt to changing environments and improve safety and performance in various tasks. This makes them valuable in complex scenarios where decisions need to be made quickly and accurately.