Safety is critical to broadening the application of reinforcement learni...
In contrast to classical reinforcement learning, distributional reinforc...
In reinforcement learning (RL), key components of many algorithms are th...
Reinforcement learning agents may sometimes develop habits that are effe...
Deep model-based Reinforcement Learning (RL) has shown super-human
perfo...
Due to its high sample complexity, simulation is, as of today, critical ...
Markov decision processes are a ubiquitous formalism for modelling syste...
Learning effective policies for real-world problems is still an open
cha...
In active perception tasks, an agent aims to select sensory actions that...
In cooperative multi-agent sequential decision making under uncertainty,...
Recent years have seen the development of methods for multiagent plannin...
This article presents the state-of-the-art in optimal solution methods f...
Cooperative Bayesian games (BGs) can model decision-making problems for ...
Decision-theoretic planning is a popular approach to sequential decision...
Efficient collaborative decision making is an important challenge for
mu...