Autonomous robots are increasingly served in real-world unstructured human environments
with complex long-horizon tasks, such as restaurant serving and office delivery. Task and …

Task and motion planning subject to Linear Temporal Logic (LTL) specifications in complex,
dynamic environments requires efficient exploration of many possible future worlds. Model …

The high probability of hardware failures prevents many advanced robots (eg, legged
robots) from being confidently deployed in real-world situations (eg, post-disaster rescue) …

Verifying the safety of autonomous vehicles is one of the major challenges towards their
deployment on public roads due to the vast number of possible situations that can occur in …

Inspired by recent successes of Monte-Carlo tree search (MCTS) in a number of artificial
intelligence (AI) application domains, we propose a reinforcement learning (RL) technique …

Abstract Monte-Carlo Tree Search (MCTS) is the state-of-the-art online planning algorithm
for large problems with discrete action spaces. However, many real-world problems involve …

In this chapter, we introduce combinatorial games such as chess and Go and take Gomoku
as an example to introduce the AlphaZero algorithm, a general algorithm that has achieved …

Decision making under uncertainty is a challenging task, especially when dealing with
complex robotics scenarios. The Partially Observable Markov Decision Process (POMDP) …

In this thesis, I studied sequential decision making problems, with a focus on the unit
commitment problem. Traditionnaly solved by dynamic programming methods, this problem …

Abstract Monte-Carlo Tree Search (MCTS) is largely responsible for the improvement not
only of many computer games, including Go and General Game Playing (GPP), but also of …