ADAPTIVE VECTOR QUANTIZATION FOR REINFORCEMENT LEARNING

Adaptive Vector Quantization for Reinforcement Learning

Dynamic programming methods are capable of solving reinforcement learning problems, in which an agent must improve its behavior through trial-and-error interactions with a dynamic environment. However, these computational algorithms suffer from the curse of dimensionality (Bellman, 1957) that the number of computational operations increases exponentially with the cardinality of the state space....

Vector Quantization applied to Reinforcement Learning

VQQL. Applying Vector Quantization to Reinforcement Learning

Reinforcement learning has proven to be a set of successful techniques for finding optimal policies on uncertain and/or dynamic domains, such as the RoboCup. One of the problems on using such techniques appears with large state and action spaces, as it is the case of input information coming from the Robosoccer simulator. In this paper, we describe a new mechanism for solving the states general...

Adaptive Relevance Matrices in Learning Vector Quantization

We propose a new matrix learning scheme to extend relevance learning vector quantization (RLVQ), an efficient prototype-based classification algorithm, toward a general adaptive metric. By introducing a full matrix of relevance factors in the distance measure, correlations between different features and their importance for the classification scheme can be taken into account and automated, and ...

Adaptive Metric Learning Vector Quantization for Ordinal Classification

Many pattern analysis problems require classification of examples into naturally ordered classes. In such cases, nominal classification schemes will ignore the class order relationships, which can have a detrimental effect on classification accuracy. This article introduces two novel ordinal learning vector quantization (LVQ) schemes, with metric learning, specifically designed for classifying ...