Sequential decision-making problems with multiple objectives arise naturally in practice and
pose unique challenges for research in decision-theoretic planning and learning, which has …

Noisy sensing, imperfect control, and environment changes are defining characteristics of
many real-world robot tasks. The partially observable Markov decision process (POMDP) …

This paper introduces a Monte-Carlo algorithm for online planning in large POMDPs. The
algorithm combines a Monte-Carlo update of the agent's belief state with a Monte-Carlo tree …

The past decade has seen a significant breakthrough in research on solving partially
observable Markov decision processes (POMDPs). Where past solvers could not scale …

From household appliances to applications in robotics, engineered systems involving
complex dynamics can only be as effective as the algorithms that control them. While …

Many problems in artificial intelligence require adaptively making a sequence of decisions
with uncertain outcomes under partial observability. Solving such stochastic optimization …

The development of autonomous vehicles for urban driving has seen rapid progress in the
past 30 years. This paper provides a summary of the current state of the art in autonomous …

Abstract Partially Observable Markov Decision Processes (POMDPs) provide a rich
framework for sequential decision-making under uncertainty in stochastic domains …

In this work, we consider the regret minimization problem for reinforcement learning in latent
Markov Decision Processes (LMDP). In an LMDP, an MDP is randomly drawn from a set of …

This work covers several aspects of the optimism in the face of uncertainty principle applied
to large scale optimization problems under finite numerical budget. The initial motivation for …