Opportunities for multiagent systems and multiagent reinforcement learning in traffic control

By Ana Lúcia Cetertich Bazzan. 2009

273 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering, 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. Johannes Heinrich
2. Samah El-Tantawy
3. David Silver
4. Hossam Abdelgawad
5. Baher Abdulhai
6. Sergey Levine
7. Yuxi Li
8. Pieter Abbeel

In Control and Systems Engineering

1. David Silver
2. Johannes Heinrich
3. Sergey Levine
4. Pieter Abbeel
5. Trevor Scott Standley
6. Nicolas Manfred Otto Heess
7. Sven Koenig
8. Jiaoyang Li

In Management Science and Operations Research

1. David Silver
2. Johannes Heinrich
3. Sergey Levine
4. Pieter Abbeel

Citations in the corpus

• Deep Reinforcement Learning from Self-Play in Imperfect-Information Games (2016, background): “…studying recreational games is to develop algorithms that will scale to more complex, real-world games such as airport and network security, financial and energy trading, traffic control and routing (Lambert III et al., 2005; Nevmyvaka et al., 2006; Bazzan, 2009; Tambe, 2011; Urieli and Stone, 2014).”
• Multiagent Reinforcement Learning for Integrated Network of Adaptive Traffic Signal Controllers (MARLIN-ATSC): Methodology and Large-Scale Application on Downtown Toronto (2013, background): “using reinforcement learning and game-theoretic approaches [8].”