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- Leslie Pack Kaelbling, Michael L. Littman, Andrew W. Moore
- J. Artif. Intell. Res.
- 1996

This paper surveys the eld of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the eld and a broad selection of current w ork are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through… (More)

- Dan Pelleg, Andrew W. Moore
- ICML
- 2000

- Christopher G. Atkeson, Andrew W. Moore, Stefan Schaal
- Artificial Intelligence Review
- 1997

This paper surveys locally weighted learning, a form of lazy learning and memory-based learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, assessing predictions, handling noisy data and outliers,… (More)

- Justin A. Boyan, Andrew W. Moore
- NIPS
- 1994

A straightforward approach to the curse of dimensionality in reinforcement learning and dynamic programming is to replace the lookup table with a generalizing function approximator such as a neu-ral net. Although this has been successful in the domain of backgammon , there is no guarantee of convergence. In this paper, we show that the combination of… (More)

- Andrew W. Moore, Christopher G. Atkeson
- Machine Learning
- 1993

We present a new algorithm,prioritized sweeping, for efficient prediction and control of stochastic Markov systems. Incremental learning methods such as temporal differencing and Q-learning have real-time performance. Classical methods are slower, but more accurate, because they make full use of the observations. Prioritized sweeping aims for the best of… (More)

- Leemon C. Baird, Andrew W. Moore
- NIPS
- 1998

Andrew Moore A simple learning rule is derived, the VAPS algorithm, which can be instantiated to generate a wide range of new reinforcement-learning algorithms. These algorithms solve a number of open problems, define several new approaches to reinforcement learning, and unify different approaches to reinforcement learning under a single theory. These… (More)

- Rémi Munos, Andrew W. Moore
- Machine Learning
- 2002

The problem of state abstraction is of central importance in optimal control, reinforcement learning and Markov decision processes. This paper studies the case of variable resolution state abstraction for continuous time and space, deterministic dynamic control problems in which near-optimal policies are required. We begin by defining a class of variable… (More)

- Andrew W. Moore
- NIPS
- 1998

Clustering is important in many elds including manufacturing, biology , nance, and astronomy. Mixture models are a popular approach due to their statistical foundations, and EM is a very popular method for nding mixture models. EM, however, requires many accesses of the data, and thus has been dismissed as impractical e.g. Zhang, Ra-makrishnan, & Livny,… (More)

- Christopher G. Atkeson, Andrew W. Moore, Stefan Schaal
- Artificial Intelligence Review
- 1997

Lazy learning methods provide useful representations and training algorithms for learning about complex phenomena during autonomous adaptive control of complex systems. This paper surveys ways in which locally weighted learning, a type of lazy learning, has been applied by us to control tasks. We explain various forms that control tasks can take, and how… (More)

- Andrew W. Moore
- Machine Learning
- 1993

Parti-game is a new algorithm for learning feasible trajectories to goal regions in high dimensional continuous state-spaces. In high dimensions it is essential that neither planning nor exploration occurs uniformly over a state-space. Parti-game maintains a decision-tree partitioning of state-space and applies techniques from game-theory and computational… (More)