Learning sequences of actions in collectives of autonomous agents
By Kagan Tumer, Adrian K. Agogino, David Hilton Wolpert. 2002
90 citations recorded by the source corpus.
Subfields: Artificial Intelligence, 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
- Chongjie Zhang
- Tonghan Wang
- Beining Han
- Kagan Tumer
- Adrian K. Agogino
- Heng Dong
- Karl Tuyls
- Yihan Wang
In Management Science and Operations Research
- Kagan Tumer
- Adrian K. Agogino
- Chongjie Zhang
- Karl Tuyls
- Gerhard Weiss
- David Hilton Wolpert
- Beining Han
- Tonghan Wang
Citations in the corpus
- Multiagent Learning: Basics, Challenges, and Prospects (2012, background): “…learn to behave optimally in the presence of other (learning) agents, facing the complexities of incomplete information, large state spaces, credit assignment, cooperative or competitive settings, and reward shaping (Wolpert and Tumer 2001; Tumer, Agogino, and Wolpert 2002; Agogino and Tumer 2008).”
- A Survey of Collectives (2004, methodology): “We have also successfully applied COIN techniques to the problem of coordinating a set of autonomous rovers so as to maximize the importance-weighted value of a set of locations they visit [226].”
- Unifying temporal and structural credit assignment problems (2004, methodology): “Many structural credit assignment problems in multi-agent systems have been successfully addressed with multi-agent utilities [4, 15, 11, 1, 7, 12].”
- Analyzing and visualizing multiagent rewards in dynamic and stochastic domains (2008, methodology): “Creating high-performance reward structures that promote coordination is a critical step in designing successful multiagent learning algorithms [8,15,18,21,22,26,31].”
- Off-Policy Multi-Agent Decomposed Policy Gradients (2020, methodology): “However, they leave the issue of credit assignment (Tumer et al., 2002; Agogino & Tumer, 2004) largely untouched in fully cooperative settings, where agents learn policies from a single global reward signal.”
- Solving Transition Independent Decentralized Markov Decision Processes (2004, background): “Wolpert, Wheeler, and Tumer (1999) and Tumer, Agogino, and Wolpert (2002) also study coordination in a decision-theoretic framework with their work on Collective Intelligence.”