Predictive feature selection for genetic policy search
| LEADER | caa a22 4500 | ||
|---|---|---|---|
| 001 | 605514844 | ||
| 003 | CHVBK | ||
| 005 | 20210128100706.0 | ||
| 007 | cr unu---uuuuu | ||
| 008 | 210128e20150901xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.1007/s10458-014-9268-y |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s10458-014-9268-y | ||
| 245 | 0 | 0 | |a Predictive feature selection for genetic policy search |h [Elektronische Daten] |c [Steven Loscalzo, Robert Wright, Lei Yu] |
| 520 | 3 | |a 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 PFS-NEAT. In an empirical study, we demonstrate that PFS-NEATis 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-NEAT, and SAFS-NEAT, in several variants of these environments. | |
| 540 | |a The Author(s), 2014 | ||
| 690 | 7 | |a Genetic policy search |2 nationallicence | |
| 690 | 7 | |a Feature selection |2 nationallicence | |
| 690 | 7 | |a Dimensionality reduction |2 nationallicence | |
| 690 | 7 | |a Reinforcement learning |2 nationallicence | |
| 700 | 1 | |a Loscalzo |D Steven |u AFRL Information Directorate, 26 Electronic Parkway, 13441, Rome, NY, USA |4 aut | |
| 700 | 1 | |a Wright |D Robert |u Binghamton University, 4400 Vestal Parkway East, 13902, Binghamton, NY, USA |4 aut | |
| 700 | 1 | |a Yu |D Lei |u Binghamton University, 4400 Vestal Parkway East, 13902, Binghamton, NY, USA |4 aut | |
| 773 | 0 | |t Autonomous Agents and Multi-Agent Systems |d Springer US; http://www.springer-ny.com |g 29/5(2015-09-01), 754-786 |x 1387-2532 |q 29:5<754 |1 2015 |2 29 |o 10458 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s10458-014-9268-y |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s10458-014-9268-y |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Loscalzo |D Steven |u AFRL Information Directorate, 26 Electronic Parkway, 13441, Rome, NY, USA |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Wright |D Robert |u Binghamton University, 4400 Vestal Parkway East, 13902, Binghamton, NY, USA |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Yu |D Lei |u Binghamton University, 4400 Vestal Parkway East, 13902, Binghamton, NY, USA |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Autonomous Agents and Multi-Agent Systems |d Springer US; http://www.springer-ny.com |g 29/5(2015-09-01), 754-786 |x 1387-2532 |q 29:5<754 |1 2015 |2 29 |o 10458 | ||