Constrained Reinforcement Learning

In 2023, I had the opportunity to work with PhD student Matt Landers at the HAI Lab on his project developing novel approaches to constrained reinforcement learning. Specifically, we aimed to find better ways of achieving knowledge transfer when the parameters of an environment may change. I helped implement several state of the art models, including policy distillation. The source code is available here.