Portfolio optimization has always been a challenging proposition in finance and
management. Portfolio optimization facilitates in selection of portfolios in a volatile market …

Effectively leveraging large, previously collected datasets in reinforcement learn-ing (RL) is
a key challenge for large-scale real-world applications. Offline RL algorithms promise to …

In reinforcement learning (RL), it is easier to solve a task if given a good representation.
While deep RL should automatically acquire such good representations, prior work often …

Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has
led to a dramatic increase in the number of applications and methods. Recent works have …

We prove that a randomly initialized neural network of* any architecture* has its Tangent
Kernel (NTK) converge to a deterministic limit, as the network widths tend to infinity. We …

A widely-used actor-critic reinforcement learning algorithm for continuous control, Deep
Deterministic Policy Gradients (DDPG), suffers from the overestimation problem, which can …

Learning from offline task demonstrations is a problem of great interest in robotics. For
simple short-horizon manipulation tasks with modest variation in task instances, offline …

Deep reinforcement learning can learn effective policies for a wide range of tasks, but is
notoriously difficult to use due to instability and sensitivity to hyperparameters. The reasons …

In this chapter, we introduce the fundamentals of classical reinforcement learning and
provide a general overview of deep reinforcement learning. We first start with the basic …

Whittle index policy is a heuristic to the intractable restless multi-armed bandits (RMAB)
problem. Although it is provably asymptotically optimal, finding Whittle indices remains …