Multi-agent RL explained
The multi-agent setup is a natural extension of the familiar RL model that we covered in Chapter 1, What Is Reinforcement Learning?, In the normal RL setup, we have one agent communicating with the environment using the observation, reward, and actions. But in some problems, which often arise in reality, we have several agents involved in the environment interaction. To give some concrete examples:
- A chess game, when our program tries to beat the opponent
- A market simulation, like product advertisements or price changes, when our actions might lead to counter-actions from other participants
- Multiplayer games, like Dota2 or StarCraft II, when the agent needs to control several units competing with other players' units
If other agents are outside of our control, we can treat them as part of the environment and still stick to the normal RL model with the single agent. But sometimes, that's too limited and not exactly what we want...
