Deep Reinforcement Learning in Action

Chapter 9. Multi-agent reinforcement learning

This chapter covers

• Why ordinary Q-learning can fail in the multi-agent setting
• How to deal with the “curse of dimensionality” with multiple agents
• How to implement multi-agent Q-learning models that can perceive other agents
• How to scale multi-agent Q-learning by using the mean field approximation
• How to use DQNs to control dozens of agents in a multi-agent physics simulation and game

So far, the reinforcement learning algorithms we have covered—Q-learning, policy gradients, and actor-critic algorithms—have all been applied to control a single agent in an environment. But what about situations where we want to control multiple agents that can interact with each other? The simplest example of this ...