Multi-Agent Deep Reinforcement Learning with Emergent Communication

• Type: Article in International Conference Proceedings Book
• Date: 2019

Authors

David Simões (Author) Other; Nuno Lau (Author) Other; Luís Paulo Reis (Author) FEUP

Conference proceedings International

Title: Proceedings of the International Joint Conference on Neural Networks

Pages: 1-8

2019 International Joint Conference on Neural Networks, IJCNN 2019

14 July 2019 through 19 July 2019

Other information

Authenticus ID: P-00R-ZYE

DOI: 10.1109/IJCNN.2019.8852293

Abstract (EN): When compared with their single-agent counterpart, multi-agent systems have an additional set of challenges for reinforcement learning algorithms, including increased complexity, non-stationary environments, credit assignment, partial observability, and achieving coordination. Deep reinforcement learning has been shown to achieve successful policies through implicit coordination, but does not handle partial-observability. This paper describes a deep reinforcement learning algorithm, based on multi-agent actor-critic, that simultaneously learns action policies for each agent, and communication protocols that compensate for partial-observability and help enforce coordination. We also research the effects of noisy communication, where messages can be late, lost, noisy, or jumbled, and how that affects the learned policies. We show how agents are able to learn both high-level policies and complex communication protocols for several different partially-observable environments. We also show how our proposal outperforms other state-of-the-art algorithms that don't take advantage of communication, even with noisy communication channels. © 2019 IEEE.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 8