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Predictive State Representations: A New Theory for Modeling Dynamical Systems
Satinder Singh, Michael James, Matthew Rudary
Modeling dynamical systems, both for control purposes and to make predictions about their behavior, is ubiquitous in science and engineering. Predictive state representations (PSRs…
Predictive Linear-Gaussian Models of Stochastic Dynamical Systems
Matthew Rudary, Satinder Singh, David Wingate
Models of dynamical systems based on predictive state representations (PSRs) are defined strictly in terms of observable quantities, in contrast with traditional models (such as Hi…
Optimal Coordinated Planning Amongst Self-Interested Agents with Private State
Ruggiero Cavallo, David C. Parkes, Satinder Singh
Consider a multi-agent system in a dynamic and uncertain environment. Each agent's local decision problem is modeled as a Markov decision process (MDP) and agents must coordinate o…
Knowledge Combination in Graphical Multiagent Model
Quang Duong, Michael P. Wellman, Satinder Singh
A graphical multiagent model (GMM) represents a joint distribution over the behavior of a set of agents. One source of knowledge about agents' behavior may come from gametheoretic…
Variance-Based Rewards for Approximate Bayesian Reinforcement Learning
Jonathan Sorg, Satinder Singh, Richard L. Lewis
The explore{exploit dilemma is one of the central challenges in Reinforcement Learning (RL). Bayesian RL solves the dilemma by providing the agent with information in the form of a…