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» Acting Optimally in Partially Observable Stochastic Domains
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GLOBECOM
2009
IEEE
14 years 9 months ago
Minimum-Length Scheduling for Multicast Traffic under Channel Uncertainty
Abstract--We consider a set of multicast sources, each multicasting a finite amount of data to its corresponding destinations. The objective is to minimize the time to deliver all ...
Anna Pantelidou, Anthony Ephremides
ATAL
2007
Springer
15 years 5 months ago
Letting loose a SPIDER on a network of POMDPs: generating quality guaranteed policies
Distributed Partially Observable Markov Decision Problems (Distributed POMDPs) are a popular approach for modeling multi-agent systems acting in uncertain domains. Given the signi...
Pradeep Varakantham, Janusz Marecki, Yuichi Yabu, ...
ECAI
2010
Springer
15 years 22 days ago
The Dynamics of Multi-Agent Reinforcement Learning
Abstract. Infinite-horizon multi-agent control processes with nondeterminism and partial state knowledge have particularly interesting properties with respect to adaptive control, ...
Luke Dickens, Krysia Broda, Alessandra Russo
IJCAI
2001
15 years 1 months ago
R-MAX - A General Polynomial Time Algorithm for Near-Optimal Reinforcement Learning
R-max is a very simple model-based reinforcement learning algorithm which can attain near-optimal average reward in polynomial time. In R-max, the agent always maintains a complet...
Ronen I. Brafman, Moshe Tennenholtz
ATAL
2008
Springer
15 years 1 months ago
Continual collaborative planning for mixed-initiative action and interaction
Multiagent environments are often highly dynamic and only partially observable which makes deliberative action planning computationally hard. In many such environments, however, a...
Michael Brenner