Probabilistic reachable sets of stochastic nonlinear systems with contextual uncertainties

no code implementations • 19 Mar 2024 • Xun Shen, Ye Wang, Kazumune Hashimoto, Yuhu Wu, Sebastien Gros

The existing methods of computing probabilistic reachable sets normally assume that the uncertainties are independent of the state.

Density Estimation

Deep Reinforcement Learning for Stabilization of Large-scale Probabilistic Boolean Networks

no code implementations • 21 Oct 2022 • Sotiris Moschoyiannis, Evangelos Chatzaroulas, Vytenis Sliogeris, Yuhu Wu

We focus on an integrative framework powered by a model-free deep RL method that can address different flavours of the control problem (e. g., with or without control inputs; attractor state or a subset of the state space as the target domain).

reinforcement-learning Reinforcement Learning (RL)