Accession Number : ADA619305


Title :   Predictive Feature Selection for Genetic Policy Search


Descriptive Note : Journal article


Corporate Author : AIR FORCE RESEARCH LAB ROME NY INFORMATION DIRECTORATE


Personal Author(s) : Loscalzo, Steven ; Wright, Robert ; Yu, Lei


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a619305.pdf


Report Date : 22 May 2014


Pagination or Media Count : 35


Abstract : Automatic learning of control policies is becoming increasingly important to allow autonomous agents to operate alongside, or in place of, humans in dangerous and fast - paced situations. Reinforcement learning (RL), including genetic policy search algorithms, comprise a promising technology area capable of learning such control policies. Unfortunately, RL techniques can take prohibitively long to learn a sufficiently good control policy in environments described by many sensors (features). We argue that in many cases only a subset of available features are needed to learn the task at hand, since others may represent irrelevant or redundant information. In this work, we propose a predictive feature selection framework that analyzes data obtained during execution of a genetic policy search algorithm to identify relevant features on - line. This serves to constrain the policy search space and reduces the time needed to locate a sufficiently good policy by embedding feature selection into the process of learning a control policy. We explore this framework through an instantiation called predictive feature selection embedded in neuroevolution of augmenting topology (NEAT), or P FS - NEAT. In an empirical study, we demonstrate that PFS - NEAT is capable of enabling NEAT to successfully find good control policies in two benchmark environments, and show that it can outperform three competing feature selection algorithms, FS - NEAT, FD - NEA T, and SAFS - NEAT, in several variants of these environments.


Descriptors :   *FEATURE EXTRACTION , *LEARNING MACHINES , *MULTIAGENT SYSTEMS , ALGORITHMS , AUGMENTATION , BAYES THEOREM , COGNITION , COLLABORATIVE TECHNIQUES , COMPUTER COMMUNICATIONS , DECISION MAKING , EXPERIMENTAL DESIGN , GAUSSIAN NOISE , MAN COMPUTER INTERFACE , MULTISENSORS , NETWORK TOPOLOGY , NEURAL NETS , OPTIMIZATION , PATTERN RECOGNITION , PERFORMANCE(ENGINEERING) , POLICIES , PROBABILITY , REDUNDANCY


Subject Categories : Statistics and Probability
      Computer Programming and Software
      Cybernetics
      Human Factors Engineering & Man Machine System


Distribution Statement : APPROVED FOR PUBLIC RELEASE