Decentralized Learning in Markov Games

By Peter Vrancx, Katja Verbeeck, Ann Nowé. 2008

80 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Management Science and Operations Research

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

1. Yann-Michaël De Hauwere
2. Sam Devlin
3. Ann Nowé
4. Daniel Kudenko
5. Peter Vrancx
6. Yujing Hu
7. Yang Gao
8. Julian Reiss

In Computer Networks and Communications

1. Yann-Michaël De Hauwere
2. Ann Nowé
3. Peter Vrancx

In Management Science and Operations Research

1. Yann-Michaël De Hauwere
2. Ann Nowé
3. Sam Devlin
4. Daniel Kudenko
5. Peter Vrancx
6. Yujing Hu
7. Yang Gao
8. Julian Reiss

Citations in the corpus

• Combining reward shaping and hierarchies for scaling to large multiagent systems (2016, unknown): “…have been presented to address these issues including organizational structures, shaped rewards, learning automata, and coordination graphs (Horling & Lesser, 2005; Tambe et al ., 2005; Xu et al ., 2005; Kok & Vlassis, 2006; Vrancx et al ., 2008; Barrett et al ., 2011; Howley & Duggan, 2011).”
• Context-sensitive reward shaping for sparse interaction multi-agent systems (2016, background): “Alternatively, in the non-cooperative case one typically tries to learn an equilibrium between agent policies (Greenwald & Hall, 2003; Hu & Wellman, 2003; Vrancx et al ., 2008).”
• Game Theory and Multi-agent Reinforcement Learning (2012, methodology): “We focus on an algorithm called Interconnected Learning Automata for Markov Games (MG-ILA)(Vrancx et al, 2008b), based on the learning automata from Section 14.2.2.3 and which can be applied to average reward Markov games.”
• Learning multi-agent state space representations (2010, unknown): “In the noncooperative case typically one tries to learn an equilibrium between agent policies [9, 7, 16].”