Deep imitation learning for 3D navigation tasks
- Submitting institution
-
Oxford Brookes University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 185899360
- Type
- D - Journal article
- DOI
-
10.1007/s00521-017-3241-z
- Title of journal
- Neural Computing and Applications
- Article number
- -
- First page
- 389
- Volume
- 29
- Issue
- -
- ISSN
- 0941-0643
- Open access status
- Compliant
- Month of publication
- December
- Year of publication
- 2017
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
-
-
- Citation count
- 8
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work proposes novel algorithms for deep active imitation learning and combing learning from demonstrations and experience using deep networks. This research provides a significant insight into how a generic learning process can be used to learn from raw data without prior knowledge of the task. The rigorous experimentation and comparison with the state of the art methods such as deep-Q-networks (DQN) and Asynchronous actor-critic (A3C) learning show that the proposed approaches are effective and efficient in 3D navigation tasks. The article highly influenced researchers for further contribution to knowledge (e.g., DOI:10.1109/ICTAI.2019.00242, DOI:10.1109/ICMLA.2019.00043, DOI:10.1109/SCORED.2019.8896352).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -