In recent years, deep learning has garnered tremendous success in a variety of application
domains. This new field of machine learning has been growing rapidly and has been …

Deep learning has demonstrated tremendous success in variety of application domains in
the past few years. This new field of machine learning has been growing rapidly and applied …

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 …

When applying autonomous driving technology to real-world scenarios, environmental
uncertainties make the development of decision-making algorithms difficult. Modeling the …

Real-world decision problems often have multiple, possibly conflicting, objectives. In multi-
objective reinforcement learning, the effects of actions in terms of these objectives must be …

Due to its perceptual limitations, an agent may have too little information about the
environment to act optimally. In such cases, it is important to keep track of the action …

Newcomblike decision problems have been studied extensively in the decision theory
literature, but they have so far been largely absent in the reinforcement learning literature. In …

Advances in reinforcement learning research have recently produced agents that are
competent, or sometimes exceed human performance, in complex tasks. Most interesting …

The standard approach for Partially Observable Markov Decision Processes (POMDPs) is to
convert them to a fully observed belief-state MDP. However, the belief state depends on the …

In this article, we consider a subclass of partially observable Markov decision process
(POMDP) problems which we termed confounding POMDPs. In these types of POMDPs …