Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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
Real-time System Control with Deep Reinforcement Learning
KTH, School of Engineering Sciences (SCI).
KTH, School of Engineering Sciences (SCI).
2018 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Reglering i realtid med förstärkningsinlärning (Swedish)
Abstract [en]

We reproduce the Deep Deterministic Policy Gradient algorithm presented in the paper Continuous Control With

Deep Reinforcement Learning to verify its results. We also strive to explain the necessary machine learning framework needed to understand the algorithm. It is a model-free, actor-critic algorithm that implements target networks and mini batch learning from a replay buffer to increase stability. Batch normalisation is introduced to make the algorithm versatile and applicable to multiple environments with varying value ranges and physical units. We use neural networks as function approximators to handle the large state and action spaces. We can show that the algorithm can learn and solve multiple environments using the same set up. After proper training the algorithm has produced a real-time decision policy which acts optimally in any state given that the environment is not too sensitive to noise.

Place, publisher, year, edition, pages
2018. , p. 10
Series
TRITA-SCI-GRU ; 2018-133
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-230728OAI: oai:DiVA.org:kth-230728DiVA, id: diva2:1218958
Supervisors
Examiners
Available from: 2018-06-15 Created: 2018-06-15 Last updated: 2018-06-15Bibliographically approved

Open Access in DiVA

fulltext(893 kB)25 downloads
File information
File name FULLTEXT01.pdfFile size 893 kBChecksum SHA-512
a7951f234ee789ce47258a61edfcee2290a176b394873ec017f3d654634584814851027a05480a3c89356a4bdef41e9eb5c40c62b4c4c05424b3bbb7f5b432a5
Type fulltextMimetype application/pdf

By organisation
School of Engineering Sciences (SCI)
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar
Total: 25 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 35 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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