With the goal to generate more scalable algorithms with higher efficiency and fewer open parameters, reinforcement learning (RL) has recently moved towards combining classical tec...
We introduce TiMDPpoly, an algorithm designed to solve planning problems with durative actions, under probabilistic uncertainty, in a non-stationary, continuous-time context. Miss...
We address the combinatorial optimization problem of finding the most influential nodes on a large-scale social network for two widely-used fundamental stochastic diffusion models...
Decentralized control of a cooperative multi-agent system is the problem faced by multiple decision-makers that share a common set of objectives. The decision-makers may be robots...
Abstract. We present an implementation of model-based online reinforcement learning (RL) for continuous domains with deterministic transitions that is specifically designed to achi...