WhiRL is focused on reinforcement learning, deep learning, and related areas. A few of the most prominent topics are listed below:
Reinforcement Learning algorithms are typically trained from scratch, starting with a random behaviour policy. This approach however often requires millions of environment interactions before learning to perform seemingly simple tasks such as playing a game of PacMan. One way to induce prior knowledge and thus accelerate learning is via meta-learning, or learning to learn. We are developing algorithms that allow an agent to make use of knowledge and skills it has obtained in related tasks, to learn faster and quickly infer which task it should solve.
Robust Reinforcement Learning
We are developing new reinforcement learning methods that are robust to significant rare events, i.e., events with low probability that nonetheless significantly affect expected performance. For example, some rare wind conditions may increase the risk of crashing a helicopter. Since crashes are so catastrophic, avoiding them is key to maximising expected performance, even though the wind conditions contributing to the crash occur only rarely. We have developed a method that uses Bayesian optimisation and quadrature to efficiently optimise policies in such settings. We have also developed an off-environment reinforcement learning method that enables policy gradient methods to work in such settings too.
We are developing decision-theoretic methods for helping perception systems, such as multi-camera tracking systems, to make efficient use of scarce resources such as computation and bandwidth. By exploiting submodularity, we can efficiently determine which subset of cameras to use, or which subset of pixel boxes in an image to process, so as to maximise metrics such as information gain and expected coverage. We have developed active perception methods with PAC guarantees and methods for active perception POMDPs.