Biases for Emergent Communication in Multi-agent Reinforcement Learning

TL;DR

This paper introduces inductive biases to facilitate emergent communication in multi-agent reinforcement learning, improving learning efficiency and performance in complex environments.

Contribution

It proposes specific inductive biases for positive signalling and listening, demonstrating their effectiveness in both simple and extended environments.

Findings

Biases ease the joint exploration problem.

Agents with biases outperform unbiased agents.

Communication protocols become more effective with biases.

Abstract

We study the problem of emergent communication, in which language arises because speakers and listeners must communicate information in order to solve tasks. In temporally extended reinforcement learning domains, it has proved hard to learn such communication without centralized training of agents, due in part to a difficult joint exploration problem. We introduce inductive biases for positive signalling and positive listening, which ease this problem. In a simple one-step environment, we demonstrate how these biases ease the learning problem. We also apply our methods to a more extended environment, showing that agents with these inductive biases achieve better performance, and analyse the resulting communication protocols.