RNN for uPOMDPs Verifiable RNN-Based Policies for Uncertain POMDPs safely transferring reinforcement learning agents under adversarial attacks ongoing Sensitivity-guided Exploration for Learning Markov Chains Mees Meuwissen Robust Permisive Policies for Interval Markov Decision Processes Bram Pellen Verifying Inter-Process Communication Between ASML Components Renato Feroce Improving Deep Q-Learning Performance through Imitation Learning Bas Neeleman Card Games as Constrained Reinforcement Learning Problem Michel van Wijk complete 2023 Active Measuring in Uncertain Environments Merlijn Krale Online Planning in Many-Agent POMDPs Addressing Scaling Issues Maris Galesloot The Underlying Belief Model of Uncertain Partially Observable Markov Decision Processes Eline Bovy Safe Reinforcement Learning From Pixel Observations Using a Stochastic Latent Actor-Critic Yannick Hogewind 2022 Optimal Order Execution for FX trading Pleun Koldewijn 2021 Model Learning of Deterministic MDPs Marck van der Vegt History-based Rewards for POMDPs Serena Rietbergen Optimal Maintenance Strategies for an Industrial Scrubber System Ilse Pool Evaluating Adversarial Attack Detectors using Formal Verification Methods Reinier Joosse Grouping of Maintenance Actions on Sewer Pipes: Using Deep Reinforcement Learning and Graph Neural Networks David Kerkkamp Formal Verification in Uncertain POMDPs Nils Neerhof 2020 Approximating Black-Box Deep Neural Networks using Active Learning as a Proxy Measurement for Robustness Christoph Schmidl Entropy-guided decision making in multiple-environment Markov decision processes Marnix Suilen
