Deep Reinforcement Learning Hands-On - Second Edition

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Multi-agent RL

In the last chapter, we dived into discrete optimization problems. In this final chapter, we will discuss a relatively new direction of reinforcement learning (RL) and deep RL, which is related to the situations when multiple agents communicate in an environment.

In this chapter, we will:

• Start with an overview of the similarities and differences between the classical single-agent RL problem and multi-agent RL
• Cover the MAgent environment, which was implemented and open sourced by the Geek.AI UK/China research group
• Use MAgent to train models in different environments with several groups of agents

Multi-agent RL explained

The multi-agent setup is a natural extension of the familiar RL model that we covered in Chapter 1, ...