Sample effficient deep reinforcement learning for control

Tim de Bruin

Learning & Autonomous Control

Research output: Thesis › Dissertation (TU Delft)

482 Downloads (Pure)

3 Conference contribution
2 Article
1 Review article

6 results

Article
Experience selection in deep reinforcement learning for control
De Bruin, T., Kober, J., Tuyls, K. & Babuška, R., 2018, In: Journal of Machine Learning Research. 19, 9, 56 p., 9.
Research output: Contribution to journal › Article › Scientific › peer-review

Open Access
File
48 Citations (Scopus)

104 Downloads (Pure)
Integrating state representation learning into deep reinforcement learning
de Bruin, T., Kober, J., Tuyls, K. & Babuska, R., 2018, In: IEEE Robotics and Automation Letters. 3, 3, p. 1394-1401
Research output: Contribution to journal › Article › Scientific › peer-review

Open Access
File
86 Citations (Scopus)

84 Downloads (Pure)
Conference contribution
Improved deep reinforcement learning for robotics through distribution-based experience retention
de Bruin, T., Kober, J., Tuyls, K. & Babuska, R., 2016, Proceedings of the 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): IROS 2016. Kwon, D-S., Kang, C-G. & Suh, I. H. (eds.). Piscataway, NJ, USA: IEEE, p. 3947-3952
Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

Open Access
File
29 Citations (Scopus)

88 Downloads (Pure)
Off-policy experience retention for deep actor-critic learning
de Bruin, T., Kober, J., Tuyls, K. P. & Babuska, R., 2016, Deep Reinforcement Learning Workshop, NIPS 2016 - December 9, 2016. 9 p.
Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review
The importance of experience replay database composition in deep reinforcement learning
de Bruin, T., Kober, J., Tuyls, K. P. & Babuska, R., 2015, Deep Reinforcement Learning Workshop, NIPS 2015. 9 p.
Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review
Review article
Reinforcement learning for control: Performance, stability, and deep approximators
Buşoniu, L., de Bruin, T., Tolić, D., Kober, J. & Palunko, I., 2018, In: Annual Reviews in Control. 46, p. 8-28
Research output: Contribution to journal › Review article › peer-review

Open Access
File
247 Citations (Scopus)

98 Downloads (Pure)

Sample effficient deep reinforcement learning for control

Research output

Search results

Experience selection in deep reinforcement learning for control

Integrating state representation learning into deep reinforcement learning

Improved deep reinforcement learning for robotics through distribution-based experience retention

Off-policy experience retention for deep actor-critic learning

The importance of experience replay database composition in deep reinforcement learning

Reinforcement learning for control: Performance, stability, and deep approximators