4 citations · 9 across the 4 of their papers we have counts for
4 papers
Reinforcement Learning with Random Time Horizons
Enric Ribera Borrell, Lorenz Richter, Christof Schütte
We extend the standard reinforcement learning framework to random time horizons. While the classical setting typically assumes finite and deterministic or infinite runtimes of traj…
Learning Koopman eigenfunctions of stochastic diffusions with optimal importance sampling and ISOKANN
Alexander Sikorski, Enric Ribera Borrell, Marcus Weber
For stochastic diffusion processes the dominant eigenfunctions of the corresponding Koopman operator contain important information about the slow-scale dynamics, that is, about the…
Connecting Stochastic Optimal Control and Reinforcement Learning
Jannes Quer, Enric Ribera Borrell
In this paper the connection between stochastic optimal control and reinforcement learning is investigated. Our main motivation is to apply importance sampling to sampling rare eve…
Improving control based importance sampling strategies for metastable diffusions via adapted metadynamics
Enric Ribera Borrell, Jannes Quer, Lorenz Richter +1
Sampling rare events in metastable dynamical systems is often a computationally expensive task and one needs to resort to enhanced sampling methods such as importance sampling. Sin…