Emotional Multiagent Reinforcement Learning in Spatial Social Dilemmas

By Chao Yu, Minjie Zhang, Fenghui Ren, Guozhen Tan. 2015

72 citations recorded by the source corpus.

Subfields: Public Health, Environmental and Occupational Health, Safety Research, Sociology and Political Science

How researchers see this paper

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

In Safety Research

1. Joel Z. Leibo
2. Karl Tuyls

In Sociology and Political Science

1. Thore Graepel
2. Joel Z. Leibo
3. Marc Lanctot
4. Janusz Marecki
5. Karl Tuyls
6. Julien Pérolat
7. Julian Reiss

Citations in the corpus

• Multi-agent Reinforcement Learning in Sequential Social Dilemmas (2017, background): “…cooperation can be stabilized, e.g., direct reciprocity [4, 5, 6, 7], indirect reciprocity [8], norm enforcement [9, 10], simple reinforcement learning variants [3], multiagent reinforcement learning [11, 12, 13, 14, 15], spatial structure [16], emotions [17], and social network effects [18, 19].”
• A multi-agent reinforcement learning model of common-pool resource appropriation (2017, background): “Others study the emergence of cooperation in partially observable Markov decision processes [9, 37, 38] but rely on knowledge of the model which is unrealistic when studying independent interaction.”
• Deep Reinforcement Learning for Multiagent Systems: A Review of Challenges, Solutions, and Applications (2018, background): “social dilemma (MGSD) that has been applied to various phenomena in social science and biology [22], [52], [94].”