Hierarchical Intermittent Motor Control with Deterministic Policy Gradient
- Submitting institution
-
Brunel University London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 071-204293-14958
- Type
- D - Journal article
- DOI
-
10.1109/ACCESS.2019.2904910
- Title of journal
- Ieee Access
- Article number
- -
- First page
- 41799
- Volume
- 7
- Issue
- -
- ISSN
- 2169-3536
- Open access status
- Compliant
- Month of publication
- March
- Year of publication
- 2019
- URL
-
https://ieeexplore.ieee.org/document/8671473
- 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
- Yes
- Number of additional authors
-
5
- Research group(s)
-
1 - Artificial Intelligence (AI)
- Citation count
- 4
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This interdisciplinary study investigates a computational deep deterministic policy gradient algorithm for motor control by uniting principles from neuroscience. The successful training of a robot arm shows its potentially promising application to a wide range of real-world problems such as Industry 4.0, wheelchair, virtual games, and many others. This international collaboration has extended the knowledge exchange between UK and China and has led to continuous funding from the National Science Foundation, China (Grant 91748122) and a series of publications, e.g., IEEE Access (2020): (10.1109/ACCESS.2020.2978161) and NeuroComputing (2020) (https://doi.org/10.1016/j.neucom.2019.12.051).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
