CruxPonent

Some recent work on physical simulation sent me back to solving ordinary differential equations—and how I had little intuition for how non-trivial numerical solvers are born.

For its own sake or as part of a policy iteration scheme, evaluating policies is an important matter.

Coming from a control background, applying RL to text generation is hardly intuitive.

This post explores the theory of regularised MDPs beyond entropic regularisation (which we covered in an older post).

Entropy, divergence, mutual information, etc. are central concepts in statistical machine learning.

For better or for worse, proximal policy optimisation (PPO) algorithms and its variants are dominating the RL landscape these days.

The goal of this post is to explore from first principles the learning of belief models in partially observable MDPs.

In this second post of this series, we take a break from successor measures to focus on successor features.

Thanks to its relative simplicity and conciseness, the discounted approach to control in MDPs has come to largely prevail in the RL theory and practice landscape.

The main promise of unsupervised RL is test-time adaptation to newly specified reward functions.