QTRAN++: Improved Value Transformation for Cooperative Multi-Agent Reinforcement Learning

By Kyunghwan Son, Sungsoo Ahn, Roben Delos Reyes, Jinwoo Shin, Yung Yi. 2020

30 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Tonghan Wang
2. Christian Schröder de Witt
3. Bei Peng
4. Tabish Rashid
5. Shimon Whiteson
6. Wendelin Böhmer
7. Chenghao Li
8. Chongjie Zhang

Citations in the corpus

• Celebrating Diversity in Shared Multi-Agent Reinforcement Learning (2021, methodology): “Our method adopts the framework of centralized training with decentralized execution (CTDE) [9, 20, 4, 5, 21, 22, 6, 11].”
• FACMAC: Factored Multi-Agent Centralised Policy Gradients (2020, background): “QTRAN++ [36] addresses the gap between the empirical performance and theoretical guarantees of QTRAN.”