Discovering Temporally-Aware Reinforcement Learning Algorithms

Designing algorithms by hand is hard, so Chris Lu and Matthew Jackson talk about how to meta-learn them for reinforcement learning. Many of the concepts in this episode are interesting to meta-learning approaches as a whole, though: "how expressive can we be and still perform well?", "how can we get the necessary data to generalize?" and "how do we make the resulting algorithm easy to apply in practice?" are problems that come up for any learning-based approach to AutoML and some of the...