Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Co-Evolution of Shaping Rewards and Meta-Parameters in Reinforcement Learning
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.
2008 (English)In: Adaptive Behavior, ISSN 1059-7123, E-ISSN 1741-2633, Vol. 16, no 6, 400-412 p.Article in journal (Refereed) Published
Abstract [en]

In this article, we explore an evolutionary approach to the optimization of potential-based shaping rewards and meta-parameters in reinforcement learning. Shaping rewards is a frequently used approach to increase the learning performance of reinforcement learning, with regards to both initial performance and convergence speed. Shaping rewards provide additional knowledge to the agent in the form of richer reward signals, which guide learning to high-rewarding states. Reinforcement learning depends critically on a few meta-parameters that modulate the learning updates or the exploration of the environment, such as the learning rate alpha, the discount factor of future rewards gamma, and the temperature tau that controls the trade-off between exploration and exploitation in softmax action selection. We validate the proposed approach in simulation using the mountain-car task. We also transfer shaping rewards and meta-parameters, evolutionarily obtained in simulation, to hardware, using a robotic foraging task.

Place, publisher, year, edition, pages
2008. Vol. 16, no 6, 400-412 p.
Keyword [en]
reinforcement learning; shaping rewards; meta-parameters; genetic algorithms
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-7568DOI: 10.1177/1059712308092835ISI: 000260840100004Scopus ID: 2-s2.0-55949119833OAI: oai:DiVA.org:kth-7568DiVA: diva2:12634
Note
QC 20100706. Uppdaterad från Submitted till Published 20100706.Available from: 2007-10-23 Created: 2007-10-23 Last updated: 2010-07-06Bibliographically 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

Open Access in DiVA

No full text

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Elfwing, StefanChristensen, Henrik
By organisation
Centre for Autonomous Systems, CAS
In the same journal
Adaptive Behavior
Computer Science

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 123 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf