MAGNet: Multi-agent Graph Network for Deep Multi-agent Reinforcement Learning
By Aleksandra Malysheva, Daniel Kudenko, Aleksei Shpilman. 2019
25 citations recorded by the source corpus.
Over recent years, deep reinforcement learning has shown strong successes in complex single-agent tasks, and more recently this approach has also been applied to multi-agent domains. In this paper, we propose a novel approach, called MAGNet, to multi-agent reinforcement learning that utilizes a relevance graph representation of the environment obtained by a self-attention mechanism, and a message-generation technique. We applied our MAGnet approach to the synthetic predator-prey multi-agent environment and the Pommerman game and the results show that it significantly outperforms state-of-the-art MARL solutions, including Multi-agent Deep Q-Networks (MADQN), Multi-agent Deep Deterministic Policy Gradient (MADDPG), and QMIX.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Wendelin Böhmer
- Shimon Whiteson
- Vitaly Kurin
- Christian Schröder de Witt
- Filippos Christianos
- Stefano V. Albrecht
- Bei Peng
- Tabish Rashid
In Computer Vision and Pattern Recognition
- Shimon Whiteson
- Wendelin Böhmer
- Vitaly Kurin
- Christian Schröder de Witt
- Stefano V. Albrecht
- Filippos Christianos
- Bei Peng
- Gregory Farquhar
Citations in the corpus
- Deep Coordination Graphs (2019, background): “, 2019) or as a joint critic in actor-critic frameworks (Tacchetti et al., 2019; Liu et al., 2019; Malysheva et al., 2019).”