Reinforcement learning (RL) enables agents to learn optimal policies by interacting with the environment. The agent collects experience from trial-and-error and optimises its action rules from the environment feedback. RL is getting popular in robotic systems for planning and accomplishment of complex tasks, such as robot navigation and manipulation. However, for these systems to work effectively and safely in the real world, the behaviours of RL agents need to be reliable, and interpretable.
To develop such RL agents, an in-depth understanding of the learning process, i.e., how the agent’s behaviours are developed, is the key. However, it is very challenging due to the random exploration in RL and the long-time learning process. On the other hand, visual analytics researches on providing interactive visual interfaces to facilitate analytical reasoning and has contributed to the interpretability of black-box models.
In this project, we will explore how to integrate visual analytics into the development of reliable and interpretable RL agents. Research questions include: 1) interactive visualization of the learning process to facilitate understanding of and control over RL processes; 2) visual analysis of agent’s interaction with the environment for reasoning of its behaviours; and 3) interactive integration of expert knowledge to regulate the learning process.
Keywords: reinforcement learning, visual analytics, explainable AI
Academic criteria: A 2:1 Honours undergraduate degree or a master's degree, in computing or a related subject. Applicants with appropriate professional experience are also considered. Degree-level mathematics (or equivalent) is required for research in some project areas.
Applicants for whom English is not their first language must demonstrate proficiency by obtaining an IELTS score of at least 6.5 overall, with a minimum of 6.0 in each skills component.
This project is open to students worldwide.
How to apply:
Please contact the supervisors of the project prior to submitting your application to discuss and develop an individual research proposal that builds on the information provided in this advert. Once you have developed the proposal with support from the supervisors, please submit your application following the instructions provided below
This project is accepting applications all year round, for self-funded candidates via https://www.cardiff.ac.uk/study/postgraduate/research/programmes/programme/computer-science-and-informatics
In order to be considered candidates must submit the following information:
- Supporting statement
- In the ‘Research Proposal’ section of the application enter the name of the project you are applying to and upload your Individual research proposal, as mentioned above in BOLD
- Qualification certificates and Transcripts
- Proof of Funding. For example, a letter of intent from your sponsor or confirmation of self-funded status (In the funding field of your application, insert Self-Funded)
- References x 2
- Proof of English language (if applicable)
For more information about this project, please contact Dr Jing Wu firstname.lastname@example.org
If you have any questions or need more information, please contact COMSC-PGR@cardiff.ac.uk