Efficient and stable reinforcement learning for robotics
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Vibhavari Dasagi Thesis
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Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. |
Description
Reinforcement Learning (RL) has long been used for learning behaviour through agent-collected experience, recently boosted by deep neural networks. However, typical deep RL agents require millions of training data samples, equating to days or weeks of training in simulation, and months to years in the real world. As robot experience is expensive, this magnitude of real robot training is not desirable. In this thesis, we address the issue of efficiency in RL to make it feasible option for robot learning in the real world by focusing on improvements in three key aspects: data collection, data usage and policy training.
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| ID Code: | 230761 |
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| Item Type: | QUT Thesis (PhD) |
| Supervisor: | Peynot, Thierry, Leitner, Juxi, & Roberts, Jonathan |
| Keywords: | Reinforcement Learning, Exploration, Artificial Curiosity, Robot Learning, Deep Learning |
| DOI: | 10.5204/thesis.eprints.230761 |
| Divisions: | Current > QUT Faculties and Divisions > Faculty of Engineering Current > Schools > School of Electrical Engineering & Robotics |
| Institution: | Queensland University of Technology |
| Deposited On: | 08 Jun 2022 04:38 |
| Last Modified: | 08 Jun 2022 04:38 |
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