Recent years have witnessed significant advances in reinforcement learning (RL), which
has registered tremendous success in solving various sequential decision-making problems …

Planning under uncertainty is a central problem in the study of automated sequential
decision making, and has been addressed by researchers in many different fields, including …

Planning algorithms are impacting technical disciplines and industries around the world,
including robotics, computer-aided design, manufacturing, computer graphics, aerospace …

The significantly expanded and updated new edition of a widely used text on reinforcement
learning, one of the most active research areas in artificial intelligence. Reinforcement …

Abstract Machine-learning research has been making great progress in many directions.
This article summarizes four of these directions and discusses some current open problems …

This talk proposes a very simple “baseline architecture” for a learning agent that can handle
stochastic, partially observable environments. The architecture uses reinforcement learning …

Partially observable Markov decision processes (POMDP's) model decision problems in
which an agent tries to maximize its reward in the face of limited and/or noisy sensor …

Recently, deep reinforcement learning has achieved great success by integrating deep
learning models into reinforcement learning algorithms in various applications such as …

Partially observable Markov decision processes (POMDPs) provide an elegant
mathematical framework for modeling complex decision and planning problems in …

For reinforcement learning in environments in which an agent has access to a reliable state
signal, methods based on the Markov decision process (MDP) have had many successes. In …