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Reinforcement Learning: A Survey
- L. Kaelbling, M. Littman, A. Moore
- PsychologyJournal of Artificial Intelligence Research
- 30 April 1996
Central issues of reinforcement learning are discussed, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state.
X-means: Extending K-means with Efficient Estimation of the Number of Clusters
A new algorithm is introduced that eeciently, searches the space of cluster locations and number of clusters to optimize the Bayesian Information Criterion (BIC) or the Akaike Information Criteria (AIC) measure.
Locally Weighted Learning
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, improving the quality of predictions by tuning fit parameters, and applications of locally weighted learning.
Generalization in Reinforcement Learning: Safely Approximating the Value Function
Grow-Support is introduced, a new algorithm which is safe from divergence yet can still reap the benefits of successful generalization, and which is not robust, and in even very benign cases, may produce an entirely wrong policy.
Locally Weighted Learning for Control
There are ways in which locally weighted learning, a type of lazy learning, has been applied by us to control tasks, and various forms that control tasks can take, are explained.
Efficient memory-based learning for robot control
- A. Moore
- Computer Science
A method of learning is presented in which all the experiences in the lifetime of the robot are explicitly remembered, thus permitting very quick predictions of the e ects of proposed actions and, given a goal behaviour, permitting fast generation of a candidate action.
Prioritized Sweeping: Reinforcement Learning with Less Data and Less Time
This work presents a new algorithm, prioritized sweeping, for efficient prediction and control of stochastic Markov systems, which successfully solves large state-space real-time problems with which other methods have difficulty.
Dynamic social network analysis using latent space models
This paper generalizes a successful static model of relationships into a dynamic model that accounts for friendships drifting over time and shows how to make it tractable to learn such models from data, even as the number of entities n gets large.
Active Learning for Anomaly and Rare-Category Detection
A technique is proposed to meet the challenge to identify "rare category" records in an unlabeled noisy set with help from a human expert who has a small budget of datapoints that they are prepared to categorize, which assumes a mixture model fit to the data but otherwise makes no assumptions on the particular form of the mixture components.
Accelerating exact k-means algorithms with geometric reasoning
New algorithms for the k-means clustering problem are presented that use the kd-tree data structure to reduce the large number of nearest-neighbor queries issued by the traditional algorithm.