Reinforcement Learning (RL) is a machine learning (ML) technique to learn sequential
decision-making in complex problems. RL is inspired by trial-and-error based human/animal …

We give an overview of recent exciting achievements of deep reinforcement learning (RL).
We discuss six core elements, six important mechanisms, and twelve applications. We start …

The two fields of machine learning and graphical causality arose and are developed
separately. However, there is, now, cross-pollination and increasing interest in both fields to …

Planning has been part of the core pursuit for artificial intelligence since its conception, but
earlier AI agents mostly focused on constrained settings because many of the cognitive …

Offline reinforcement learning (RL) refers to the problem of learning policies entirely from a
batch of previously collected data. This problem setting is compelling, because it offers the …

Constructing agents with planning capabilities has long been one of the main challenges in
the pursuit of artificial intelligence. Tree-based planning methods have enjoyed huge …

Designing effective model-based reinforcement learning algorithms is difficult because the
ease of data generation must be weighed against the bias of model-generated data. In this …

Machine learning in finance sits at the intersection of a number of emergent and established
disciplines including pattern recognition, financial econometrics, statistical computing …

Sequential decision making, commonly formalized as Markov Decision Process (MDP)
optimization, is an important challenge in artificial intelligence. Two key approaches to this …

Deep reinforcement learning is the combination of reinforcement learning (RL) and deep
learning. This field of research has been able to solve a wide range of complex …