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Darwinian Embodied Evolution of the Learning Ability for Survival
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
2011 (English)In: Adaptive Behavior, ISSN 1059-7123, E-ISSN 1741-2633, Vol. 19, no 2, 101-102 p.Article in journal (Refereed) Published
Abstract [en]

In this article we propose a framework for performing embodied evolution with a limited number of robots, by utilizing time-sharing in subpopulations of virtual agents hosted in each robot. Within this framework, we explore the combination of within-generation learning of basic survival behaviors by reinforcement learning, and evolutionary adaptations over the generations of the basic behavior selection policy, the reward functions, and metaparameters for reinforcement learning. We apply a biologically inspired selection scheme, in which there is no explicit communication of the individuals' fitness information. The individuals can only reproduce offspring by mating-a pair-wise exchange of genotypes-and the probability that an individual reproduces offspring in its own subpopulation is dependent on the individual's "health," that is, energy level, at the mating occasion. We validate the proposed method by comparing it with evolution using standard centralized selection, in simulation, and by transferring the obtained solutions to hardware using two real robots.

Place, publisher, year, edition, pages
2011. Vol. 19, no 2, 101-102 p.
Keyword [en]
Embodied evolution; evolutionary robotics; reinforcement learning; meta-learning; shaping rewards; metaparameters
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-7567DOI: 10.1177/1059712310397633ISI: 000289714900002Scopus ID: 2-s2.0-79955445257OAI: oai:DiVA.org:kth-7567DiVA: diva2:12633
Note

QC 20110509 ändrad från submitted till pulished 20110509

Available from: 2007-10-23 Created: 2007-10-23 Last updated: 2018-01-13Bibliographically 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 Sciences
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: 2018-01-13Bibliographically approved

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Doya, Kenji

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