Wendelin Böhmer, Tabish Rashid, Shimon Whiteson (2019), Exploration with Unreliable Intrinsic Reward in Multi-Agent Reinforcement Learning, In ICML em Exploration in Reinforcement Learning workshop Volume abs/1906.02138.

Shangtong Zhang, Wendelin Boehmer, Shimon Whiteson (2019), Generalized off-policy actor-critic, In Advances in Neural Information Processing Systems.

Dongge Han, Wendelin Böhmer, Michael Wooldridge, Alex Rogers (2019), Multi-agent Hierarchical Reinforcement Learning with Dynamic Termination, Abhaya C. Nayak, Alok Sharma (Eds.), In PRICAI 2019 Volume 11671 p.80-92, Springer.

Christian A. Schroeder de Witt, Jakob N. Foerster, Gregory Farquhar, Philip H.S. Torr, Wendelin Böhmer, Shimon Whiteson (2019), Multi-agent common knowledge reinforcement learning, In Advances in Neural Information Processing Systems.

Dongge Han, Wendelin Boehmer, Michael Wooldridge, Alex Rogers (2019), Multi-agent hierarchical reinforcement learning with dynamic termination, In 18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019 p.2006-2008, International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

Wendelin Böhmer (2017), Representation and Generalization in Autonomous Reinforcement Learning, PhD Thesis.

Rong Guo, Wendelin Böhmer, Martin Hebart, Samson Chien, Tobias Sommer, Klaus Obermayer, Jan Gläscher (2016), Interaction of Instrumental and Goal-directed Learning Modulates Prediction Error Representations in the Ventral Striatum, In Journal of Neuroscience Volume 36 p.12650-12660.

Wendelin Böhmer, Rong Guo, Klaus Obermayer (2016), Non-deterministic policy improvement stabilizes approximated reinforcement learning, In European Workshop on Reinforcement Learning.

Wendelin Böhmer, Jost Tobias Springenberg, Joschka Boedecker, Martin Riedmiller, Klaus Obermayer (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, In KI - Künstliche Intelligenz Volume 29 p.353-362.

Wendelin Böhmer, Klaus Obermayer (2015), Regression with Linear Factored Functions, In Machine Learning and Knowledge Discovery in Databases Volume 9284 p.119-134, Springer.