Multi-Agent Learning on Sensory data for Autonomous Systems
In many real world applications data is not static but arrives in data streams. On-line learning methods suits these applications by adjusting the learning policy incrementally as soon as new data arrives. In on-line settings agents never stop learning, while keep exploring the environment they learn, adapt and relearn in order to improve long term strategy. Constant exploration degrades system performance, so a balance is required between exploration and exploitation. This project aims to design a robust version of on-line reinforcement learning which overcomes the limitations of the sensory data such as missing values, outliers, and uncertainty etc. in order to improve convergence.
How to apply
If you are interested in applying for the above PhD topic please follow the steps below:
- Contact the supervisor by email or phone to discuss your interest and find out if you would be suitable. Supervisor details can be found on this topic page. The supervisor will guide you in developing the topic-specific research proposal, which will form part of your application.
- Click on the 'Apply here' button on this page and you will be taken to the relevant PhD course page, where you can apply using an online application.
- Complete the online application indicating your selected supervisor and include the research proposal for the topic you have selected.
Good luck!
This is a self funded topic
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