Convergence analysis of gradient descent stochastic algorithms
Journal of Optimization Theory and Applications
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
R-max - a general polynomial time algorithm for near-optimal reinforcement learning
The Journal of Machine Learning Research
Mathematics of Operations Research
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Robust Control of Markov Decision Processes with Uncertain Transition Matrices
Operations Research
Dynamic Programming and Optimal Control, Vol. II
Dynamic Programming and Optimal Control, Vol. II
Learning to act using real-time dynamic programming
Artificial Intelligence
Incremental learning of statistical motion patterns with growing hidden Markov models
IEEE Transactions on Intelligent Transportation Systems
Percentile Optimization for Markov Decision Processes with Parameter Uncertainty
Operations Research
A Bayesian sampling approach to exploration in reinforcement learning
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Reinforcement Learning and Dynamic Programming Using Function Approximators
Reinforcement Learning and Dynamic Programming Using Function Approximators
A Bayesian nonparametric approach to modeling motion patterns
Autonomous Robots
Online Bayesian estimation of transition probabilities for Markovian jump systems
IEEE Transactions on Signal Processing
The Kernel Least-Mean-Square Algorithm
IEEE Transactions on Signal Processing
Distributed Learning for Planning Under Uncertainty Problems with Heterogeneous Teams
Journal of Intelligent and Robotic Systems
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Solving large scale sequential decision making problems without prior knowledge of the state transition model is a key problem in the planning literature. One approach to tackle this problem is to learn the state transition model online using limited observed measurements. We present an adaptive function approximator (incremental Feature Dependency Discovery (iFDD)) that grows the set of features online to approximately represent the transition model. The approach leverages existing feature-dependencies to build a sparse representation of the state transition model. Theoretical analysis and numerical simulations in domains with state space sizes varying from thousands to millions are used to illustrate the benefit of using iFDD for incrementally building transition models in a planning framework.