An Analysis of Discretization Methods for Communication Learning with Multi-Agent Reinforcement Learning

Understand

Communication is crucial in multi-agent reinforcement learning when agents are not able to observe the full state of the environment.

• The most common approach to allow learned communication between agents is the use of a differentiable communication channel that allows gradients to flow between agents as a form of feedback.
• However, this is challenging when we want to use discrete messages to reduce the message size since gradients cannot flow through a discrete communication channel.
• Previous work proposed methods to deal with this problem.