Are you applying to universities? | SHARE YOUR EXPERIENCE Are you applying to universities? | SHARE YOUR EXPERIENCE

EPSRC/BAE Systems INDUSTRIAL CASE PhD Studentship - “Mitigation of Reinforcement Learning Algorithms in Changing Environments”

   Faculty of Humanities Doctoral Academy, School of Environment, Education and Development

This project is no longer listed on and may not be available.

Click here to search for PhD studentship opportunities
  Prof Richard Allmendinger  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

About the Project

The development of (deep) Reinforcement Learning (RL) algorithms to train agents within game environments is well known. Agent training is typically conducted against a known, simplified, or constrained environment. However, the deployed environment is typically more complex, and subject to some change and uncertainly not represented in the training environment. RL algorithms typically characterise performance against probabilistic arenas, rather than being able to cope with an environment that is subject to change over time. The performance of the resulting RL agent can then be expected to become compromised over time, but not necessarily be catastrophic. In this PhD project, we are concerned with (i) understanding this performance degradation and (ii) the development of mitigating strategies. More specifically, the project will focus at creating a train-and-test framework comprising a simulation engine for dynamic environment and a configurable RL approach. In addition to considering changes in the environment, the simulator and RL agent will need to account for real-world challenges, such as multiple conflicting objectives, robustness, and safety issues.

The team at BAE Systems is focused on cutting-edge research in advanced simulation, optimization, and machine learning, and are thus invested in how RL can be extended to support decision making in dynamic environments. The project will therefore contribute directly to BAE Systems’ ongoing research. From a scientific perspective, this project will lead to cross-disciplinary research and output that is of high quality and significance.

Due to the nature of this project, candidates may be subject to a security check.

PhD saved successfully
View saved PhDs