Nested evolution of an autonomous agent using descriptive encoding

Authors:
Jae-Yoon Jung;James A. Reggia
Affiliations:
Queen's University, Kingston, ON, Canada;University of Maryland, College Park, MD, USA
Venue:
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Year:
2008

Citing 3
Cited 1

The theory of evolution strategies

The theory of evolution strategies
Conditions enabling the evolution of inter-agent signaling in an artificial world

Artificial Life
Evolutionary Design of Neural Network Architectures Using a Descriptive Encoding Language

IEEE Transactions on Evolutionary Computation

Evolving an autonomous agent for non-Markovian reinforcement learning

Proceedings of the 11th Annual conference on Genetic and evolutionary computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we investigate the use of nested evolution in which each step of one evolutionary process involves running a second evolutionary process. We apply this approach to build a neuroevolution system for reinforcement learning (RL) problems. Genetic programming based on a descriptive encoding is used to evolve the neural architecture, while a nested evolution strategy is used to evolve the needed connection weights. We test this hierarchical evolution on a non-Markovian RL problem involving an autonomous foraging agent, finding that the evolved networks significantly outperform a rule-based agent serving as a control.