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Biologically Inspired Embodied Evolution of Survival
KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
Neural Computation Unit, Okinawa Institute of Science and Technology, Japan.
Neural Computation Unit, Okinawa Institute of Science and Technology, Japan.
KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
2005 (English)In: 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings, 2005, 2210-2216 p.Conference paper, Published paper (Refereed)
Abstract [en]

Embodied evolution is a methodology for evolutionary robotics that mimics the distributed, asynchronous and autonomous properties of biological evolution. The evaluation, selection and reproduction are carried out by and between the robots, without any need for human intervention. In this paper we propose a biologically inspired embodied evolution framework, which fully integrates self-preservation, recharging from external batteries in the environment, and self-reproduction, pair-wise exchange of genetic material, into a survival system. The individuals are, explicitly, evaluated for the performance of the battery capturing task, but also, implicitly, for the mating task by the fact that an individual that mates frequently has larger probability to spread its gene in the population. We have evaluated our method in simulation experiments and the simulation results show that the solutions obtained by our embodied evolution method were able to optimize the two survival tasks, battery capturing and mating, simultaneously. We have also performed preliminary experiments in hardware, with promising results.

Place, publisher, year, edition, pages
2005. 2210-2216 p.
Keyword [en]
Computer simulation; Evolutionary algorithms; Genes; Population statistics; Probability
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-7566DOI: 10.1109/CEC.2005.1554969ISI: 000232173100294Scopus ID: 2-s2.0-27144533954ISBN: 0-7803-9363-5 (print)OAI: oai:DiVA.org:kth-7566DiVA: diva2:12632
Conference
2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005; Edinburgh, Scotland; 2 Sept. - 5 Sept. 2005
Note
QC 20100706Available from: 2007-10-23 Created: 2007-10-23 Last updated: 2011-10-14Bibliographically approved
In thesis
1. Embodied Evolution of Learning Ability
Open this publication in new window or tab >>Embodied Evolution of Learning Ability
2007 (English)Doctoral thesis, comprehensive summary (Other scientific)
Abstract [en]

Embodied evolution is a methodology for evolutionary robotics that mimics the distributed, asynchronous, and autonomous properties of biological evolution. The evaluation, selection, and reproduction are carried out by cooperation and competition of the robots, without any need for human intervention. An embodied evolution framework is therefore well suited to study the adaptive learning mechanisms for artificial agents that share the same fundamental constraints as biological agents: self-preservation and self-reproduction.

The main goal of the research in this thesis has been to develop a framework for performing embodied evolution with a limited number of robots, by utilizing time-sharing of subpopulations of virtual agents inside each robot. The framework integrates reproduction as a directed autonomous behavior, and allows for learning of basic behaviors for survival by reinforcement learning. The purpose of the evolution is to evolve the learning ability of the agents, by optimizing meta-properties in reinforcement learning, such as the selection of basic behaviors, meta-parameters that modulate the efficiency of the learning, and additional and richer reward signals that guides the learning in the form of shaping rewards. The realization of the embodied evolution framework has been a cumulative research process in three steps: 1) investigation of the learning of a cooperative mating behavior for directed autonomous reproduction; 2) development of an embodied evolution framework, in which the selection of pre-learned basic behaviors and the optimization of battery recharging are evolved; and 3) development of an embodied evolution framework that includes meta-learning of basic reinforcement learning behaviors for survival, and in which the individuals are evaluated by an implicit and biologically inspired fitness function that promotes reproductive ability. The proposed embodied evolution methods have been validated in a simulation environment of the Cyber Rodent robot, a robotic platform developed for embodied evolution purposes. The evolutionarily obtained solutions have also been transferred to the real robotic platform.

The evolutionary approach to meta-learning has also been applied for automatic design of task hierarchies in hierarchical reinforcement learning, and for co-evolving meta-parameters and potential-based shaping rewards to accelerate reinforcement learning, both in regards to finding initial solutions and in regards to convergence to robust policies.

Place, publisher, year, edition, pages
Stockholm: KTH, 2007. vi, 61 p.
Series
Trita-CSC-A, ISSN 1653-5723 ; 2007:16
Keyword
Embodied Evolution, Evolutionary Robotics, Reinforcement Learning, Shaping Rewards, Meta-parameters, Hierarchical Reinforcement Learning
National Category
Computer Science
Identifiers
urn:nbn:se:kth:diva-4515 (URN)978-91-7178-787-3 (ISBN)
Public defence
2007-11-12, Sal F3, KTH, Lindstedtsvägen 26, Stockholm, 10:00
Opponent
Supervisors
Note
QC 20100706Available from: 2007-10-23 Created: 2007-10-23 Last updated: 2010-09-21Bibliographically approved

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