Minimizing Communication while Maximizing Performance in Multi-Agent Reinforcement Learning

by   Varun Kumar Vijay, et al.

Inter-agent communication can significantly increase performance in multi-agent tasks that require co-ordination to achieve a shared goal. Prior work has shown that it is possible to learn inter-agent communication protocols using multi-agent reinforcement learning and message-passing network architectures. However, these models use an unconstrained broadcast communication model, in which an agent communicates with all other agents at every step, even when the task does not require it. In real-world applications, where communication may be limited by system constraints like bandwidth, power and network capacity, one might need to reduce the number of messages that are sent. In this work, we explore a simple method of minimizing communication while maximizing performance in multi-task learning: simultaneously optimizing a task-specific objective and a communication penalty. We show that the objectives can be optimized using Reinforce and the Gumbel-Softmax reparameterization. We introduce two techniques to stabilize training: 50 training and message forwarding. Training with the communication penalty on only 50 messages. Second, repeating messages received previously helps models retain information, and further improves performance. With these techniques, we show that we can reduce communication by 75



There are no comments yet.


page 1

page 2

page 3

page 4


Event-Triggered Multi-agent Reinforcement Learning with Communication under Limited-bandwidth Constraint

Communicating with each other in a distributed manner and behaving as a ...

Learning Emergent Discrete Message Communication for Cooperative Reinforcement Learning

Communication is a important factor that enables agents work cooperative...

Correcting Experience Replay for Multi-Agent Communication

We consider the problem of learning to communicate using multi-agent rei...

Straggling for Covert Message Passing on Complete Graphs

We introduce a model for mobile, multi-agent information transfer that i...

Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Control

Multi-agent reinforcement learning (MARL) has recently received consider...

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

Communication is crucial in multi-agent reinforcement learning when agen...

Message Passing Multi-Agent GANs

Communicating and sharing intelligence among agents is an important face...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.