This article surveys reinforcement learning from the perspective of optimization and control,
with a focus on continuous control applications. It reviews the general formulation …

Fine-tuning language models (LMs) has yielded success on diverse downstream tasks, but
as LMs grow in size, backpropagation requires a prohibitively large amount of memory …

A high school student can create deep Q-learning code to control her robot, without any
understanding of the meaning of'deep'or'Q', or why the code sometimes fails. This book is …

In many optimization problems arising from scientific, engineering and artificial intelligence
applications, objective and constraint functions are available only as the output of a black …

Reinforcement learning is about learning agent models that make the best sequential
decisions in unknown environments. In an unknown environment, the agent needs to …

Abstract Model-free reinforcement learning aims to offer off-the-shelf solutions for controlling
dynamical systems without requiring models of the system dynamics. We introduce a model …

A common belief in model-free reinforcement learning is that methods based on random
search in the parameter space of policies exhibit significantly worse sample complexity than …

We study derivative-free methods for policy optimization over the class of linear policies. We
focus on characterizing the convergence rate of these methods when applied to linear …

We consider derivative-free algorithms for stochastic and nonstochastic convex optimization
problems that use only function values rather than gradients. Focusing on nonasymptotic …

Physically based differentiable rendering has recently evolved into a powerful tool for
solving inverse problems involving light. Methods in this area perform a differentiable …