Introducing multi-agent reinforcement learning
All of the problems and algorithms we have covered in the book so far involved a single agent being trained in an environment. On the other hand, in many applications from games to autonomous vehicle fleets, there are multiple decision-makers, agents, which train concurrently, but execute local policies (i.e., without a central decision-maker). This leads us to MARL, which involves a much richer set of problems and challenges than single-agent RL does. In this section, we give an overview of MARL landscape.
Collaboration and competition between MARL agents
MARL problems can be classified into three different groups with respect to the structure of collaboration and competition between agents. Let's look into what those groups are and what types of applications fit into each group.
Fully cooperative environments
In this setting, all of the agents in the environment work towards a common long-term goal. The agents are credited...
