Regularized Sparse Kernel Slow Feature Analysis. - Wendelin Böhmer; Steffen Grünewälder; Hannes Nickisch; Klaus Obermayer (ECML/PKDD (1), 2011)

Robot Navigation using Reinforcement Learning and Slow Feature Analysis - Wendelin Böhmer (CoRR, 2012)

Generating feature spaces for linear algorithms with regularized sparse kernel slow feature analysis. - Wendelin Böhmer; Steffen Grünewälder; Hannes Nickisch; Klaus Obermayer (Mach. Learn., 2012)

Construction of approximation spaces for reinforcement learning. - Wendelin Böhmer; Steffen Grünewälder; Yun Shen; Marek Musial; Klaus Obermayer (J. Mach. Learn. Res., 2013)

Factored Representations for Regression. - Wendelin Böhmer; Klaus Obermayer (CoRR, 2014)

Neural systems for choice and valuation with counterfactual learning signals. - Michael J. Tobia; R. Guo; U. Schwarze; Wendelin Böhmer; Jan Gläscher; B. Finckh; A. Marschner; Christian Büchel; Klaus Obermayer; Tobias Sommer (NeuroImage, 2014)

Regression with Linear Factored Functions. - Wendelin Böhmer; Klaus Obermayer (ECML/PKDD (1), 2015)

Autonomous Learning of State Representations for Control: An Emerging Field Aims to Autonomously Learn State Representations for Reinforcement Learning Agents from Their Real-World Sensor Observations. - Wendelin Böhmer; Jost Tobias Springenberg; Joschka Boedecker; Martin A. Riedmiller; Klaus Obermayer (Künstliche Intell., 2015)

Non-Deterministic Policy Improvement Stabilizes Approximated Reinforcement Learning. - Wendelin Böhmer; Rong Guo; Klaus Obermayer (CoRR, 2016)

Representation and generalization in autonomous reinforcement learning. - Wendelin Böhmer (2017)

Multi-Agent Common Knowledge Reinforcement Learning. - Christian Schröder de Witt; Jakob N. Foerster; Gregory Farquhar; Philip H. S. Torr; Wendelin Boehmer; Shimon Whiteson (NeurIPS, 2019)

Generalized Off-Policy Actor-Critic. - Shangtong Zhang; Wendelin Boehmer; Shimon Whiteson (NeurIPS, 2019)

Multi-agent Hierarchical Reinforcement Learning with Dynamic Termination. - Dongge Han; Wendelin Böhmer; Michael J. Wooldridge; Alex Rogers (PRICAI (2), 2019)

Multitask Soft Option Learning. - Maximilian Igl; Andrew Gambardella; Nantas Nardelli; N. Siddharth 001; Wendelin Böhmer; Shimon Whiteson (CoRR, 2019)

Exploration with Unreliable Intrinsic Reward in Multi-Agent Reinforcement Learning. - Wendelin Böhmer; Tabish Rashid; Shimon Whiteson (CoRR, 2019)

Deep Residual Reinforcement Learning. - Shangtong Zhang; Wendelin Boehmer; Shimon Whiteson (AAMAS, 2020)

Optimistic Exploration even with a Pessimistic Initialisation. - Tabish Rashid; Bei Peng 001; Wendelin Boehmer; Shimon Whiteson (ICLR, 2020)

Deep Coordination Graphs. - Wendelin Boehmer; Vitaly Kurin; Shimon Whiteson (ICML, 2020)

Multitask Soft Option Learning. - Maximilian Igl; Andrew Gambardella; Jinke He; Nantas Nardelli; N. Siddharth 001; Wendelin Boehmer; Shimon Whiteson (UAI, 2020)

Deep Multi-Agent Reinforcement Learning for Decentralized Continuous Cooperative Control. - Christian Schröder de Witt; Bei Peng 001; Pierre-Alexandre Kamienny; Philip H. S. Torr; Wendelin Böhmer; Shimon Whiteson (CoRR, 2020)

Privileged Information Dropout in Reinforcement Learning. - Pierre-Alexandre Kamienny; Kai Arulkumaran; Feryal M. P. Behbahani; Wendelin Boehmer; Shimon Whiteson (CoRR, 2020)

AI-QMIX: Attention and Imagination for Dynamic Multi-Agent Reinforcement Learning. - Shariq Iqbal; Christian A. Schröder de Witt; Bei Peng 001; Wendelin Böhmer; Shimon Whiteson; Fei Sha (CoRR, 2020)

The Impact of Non-stationarity on Generalisation in Deep Reinforcement Learning. - Maximilian Igl; Gregory Farquhar; Jelena Luketina; Wendelin Boehmer; Shimon Whiteson (CoRR, 2020)

Transient Non-stationarity and Generalisation in Deep Reinforcement Learning. - Maximilian Igl; Gregory Farquhar; Jelena Luketina; Wendelin Boehmer; Shimon Whiteson (ICLR, 2021)

My Body is a Cage: the Role of Morphology in Graph-Based Incompatible Control. - Vitaly Kurin; Maximilian Igl; Tim Rocktäschel; Wendelin Boehmer; Shimon Whiteson (ICLR, 2021)

UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning. - Tarun Gupta 002; Anuj Mahajan; Bei Peng 001; Wendelin Boehmer; Shimon Whiteson (ICML, 2021)

Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning. - Shariq Iqbal; Christian A. Schröder de Witt; Bei Peng 001; Wendelin Boehmer; Shimon Whiteson; Fei Sha (ICML, 2021)

Deep Residual Reinforcement Learning (Extended Abstract). - Shangtong Zhang; Wendelin Boehmer; Shimon Whiteson (IJCAI, 2021)

FACMAC: Factored Multi-Agent Centralised Policy Gradients. - Bei Peng 001; Tabish Rashid; Christian Schröder de Witt; Pierre-Alexandre Kamienny; Philip H. S. Torr; Wendelin Boehmer; Shimon Whiteson (NeurIPS, 2021)

Planning with Uncertainty: Deep Exploration in Model-Based Reinforcement Learning. - Yaniv Oren; Matthijs T. J. Spaan; Wendelin Böhmer (CoRR, 2022)

Multi-Robot Local Motion Planning Using Dynamic Optimization Fabrics. - Saray Bakker; Luzia Knödler; Max Spahn; Wendelin Böhmer; Javier Alonso-Mora (MRS, 2023)

The Role of Diverse Replay for Generalisation in Reinforcement Learning. - Max Weltevrede; Matthijs T. J. Spaan; Wendelin Böhmer (CoRR, 2023)

You Shall not Pass: the Zero-Gradient Problem in Predict and Optimize for Convex Optimization. - Grigorii Veviurko; Wendelin Böhmer; Mathijs de Weerdt (CoRR, 2023)

Lights out: training RL agents robust to temporary blindness. - Nathan Ordonez; Marije Tromp; Pau Marquez Julbe; Wendelin Böhmer (CoRR, 2023)

Active Classification of Moving Targets With Learned Control Policies. - Álvaro Serra-Gómez; Eduardo Montijano; Wendelin Böhmer; Javier Alonso-Mora (IEEE Robotics Autom. Lett., 2023)

Learning scalable and efficient communication policies for multi-robot collision avoidance. - Álvaro Serra-Gómez; Hai Zhu 002; Bruno Brito; Wendelin Böhmer; Javier Alonso-Mora (Auton. Robots, 2023)

Diverse Projection Ensembles for Distributional Reinforcement Learning. - Moritz Akiya Zanger; Wendelin Boehmer; Matthijs T. J. Spaan (ICLR, 2024)

To the Max: Reinventing Reward in Reinforcement Learning. - Grigorii Veviurko; Wendelin Boehmer; Mathijs de Weerdt (ICML, 2024)

A Penalty-Based Guardrail Algorithm for Non-Decreasing Optimization with Inequality Constraints. - Ksenija Stepanovic; Wendelin Böhmer; Mathijs de Weerdt (CoRR, 2024)

Value Improved Actor Critic Algorithms. - Yaniv Oren; Moritz A. Zanger; Pascal R. van der Vaart; Matthijs T. J. Spaan; Wendelin Böhmer (CoRR, 2024)

Explore-Go: Leveraging Exploration for Generalisation in Deep Reinforcement Learning. - Max Weltevrede; Felix Kaubek; Matthijs T. J. Spaan; Wendelin Böhmer (CoRR, 2024)

Generalisation to unseen topologies: Towards control of biological neural network activity. - Laurens Engwegen; Daan Brinks; Wendelin Böhmer (CoRR, 2024)

Training on more Reachable Tasks for Generalisation in Reinforcement Learning. - Max Weltevrede; Caroline Horsch; Matthijs T. J. Spaan; Wendelin Böhmer (CoRR, 2024)

Distributed multi-target tracking and active perception with mobile camera networks. - Sara Casao; Álvaro Serra-Gómez; Ana C. Murillo; Wendelin Böhmer; Javier Alonso-Mora; Eduardo Montijano (Comput. Vis. Image Underst., 2024)

Epistemic Monte Carlo Tree Search. - Yaniv Oren; Viliam Vadocz; Matthijs T. J. Spaan; Wendelin Boehmer (ICLR, 2025)

TamedPUMA: safe and stable imitation learning with geometric fabrics. - Saray Bakker; Rodrigo Pérez-Dattari; Cosimo Della Santina; Wendelin Böhmer; Javier Alonso-Mora (L4DC, 2025)

Contextual Similarity Distillation: Ensemble Uncertainties with a Single Model. - Moritz A. Zanger; Pascal R. van der Vaart; Wendelin Böhmer; Matthijs T. J. Spaan (CoRR, 2025)

How Ensembles of Distilled Policies Improve Generalisation in Reinforcement Learning. - Max Weltevrede; Moritz A. Zanger; Matthijs T. J. Spaan; Wendelin Böhmer (CoRR, 2025)

Universal Value-Function Uncertainties. - Moritz A. Zanger; Max Weltevrede; Yaniv Oren; Pascal R. van der Vaart; Caroline Horsch; Wendelin Böhmer; Matthijs T. J. Spaan (CoRR, 2025)

Modular Recurrence in Contextual MDPs for Universal Morphology Control. - Laurens Engwegen; Daan Brinks; Wendelin Böhmer (CoRR, 2025)