Multi-Agent Systems by Incremental Gradient Reinforcement Learning

By Alain Dutech, Olivier Buffet, François Charpillet. 2001

45 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. Chris Amato
2. Sean Luke
3. Shayegan Omidshafiei
4. Jonathan P. How
5. Liviu Panait
6. Gregory Palmer
7. Jason Pazis
8. John L. Vian

Citations in the corpus

• Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability (2017, methodology): “Works include model-free gradient-ascent based methods (Peshkin et al., 2000; Dutech et al., 2001), simulator-supported methods to improve policies using a series of linear programs (Wu et al.”
• Cooperative Multi-Agent Learning: The State of the Art (2005, background): “[75] present RL approaches for a similar problem domain where agents receive rewards only when they merge together pucks of different colors.”