Wendelin Böhmer

According to our database, Wendelin Böhmer authored at least 39 papers between 2011 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

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., January, 2024

To the Max: Reinventing Reward in Reinforcement Learning.

Grigorii Veviurko, Wendelin Böhmer, Mathijs de Weerdt

CoRR, 2024

2023

Learning scalable and efficient communication policies for multi-robot collision avoidance.

Álvaro Serra-Gómez, Hai Zhu, Bruno Brito, Wendelin Böhmer, Javier Alonso-Mora

Auton. Robots, December, 2023

Active Classification of Moving Targets With Learned Control Policies.

Álvaro Serra-Gómez, Eduardo Montijano, Wendelin Böhmer, Javier Alonso-Mora

IEEE Robotics Autom. Lett., June, 2023

Lights out: training RL agents robust to temporary blindness.

Nathan Ordonez, Marije Tromp, Pau Marquez Julbe, 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

Diverse Projection Ensembles for Distributional Reinforcement Learning.

Moritz A. Zanger, Wendelin Böhmer, Matthijs T. J. Spaan

CoRR, 2023

The Role of Diverse Replay for Generalisation in Reinforcement Learning.

Max Weltevrede, Matthijs T. J. Spaan, Wendelin Böhmer

CoRR, 2023

Multi-Robot Local Motion Planning Using Dynamic Optimization Fabrics.

Saray Bakker, Luzia Knödler, Max Spahn, Wendelin Böhmer, Javier Alonso-Mora

Proceedings of the International Symposium on Multi-Robot and Multi-Agent Systems, 2023

2022

Planning with Uncertainty: Deep Exploration in Model-Based Reinforcement Learning.

Yaniv Oren, Matthijs T. J. Spaan, Wendelin Böhmer

CoRR, 2022

2021

FACMAC: Factored Multi-Agent Centralised Policy Gradients.

Bei Peng, Tabish Rashid, Christian Schröder de Witt, Pierre-Alexandre Kamienny, Philip H. S. Torr, Wendelin Boehmer, Shimon Whiteson

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Deep Residual Reinforcement Learning (Extended Abstract).

Shangtong Zhang, Wendelin Boehmer, Shimon Whiteson

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning.

Shariq Iqbal, Christian A. Schröder de Witt, Bei Peng, Wendelin Boehmer, Shimon Whiteson, Fei Sha

Proceedings of the 38th International Conference on Machine Learning, 2021

UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning.

Tarun Gupta, Anuj Mahajan, Bei Peng, Wendelin Boehmer, Shimon Whiteson

Proceedings of the 38th International Conference on Machine Learning, 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

Proceedings of the 9th International Conference on Learning Representations, 2021

Transient Non-stationarity and Generalisation in Deep Reinforcement Learning.

Maximilian Igl, Gregory Farquhar, Jelena Luketina, Wendelin Boehmer, Shimon Whiteson

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

The Impact of Non-stationarity on Generalisation in Deep Reinforcement Learning.

Maximilian Igl, Gregory Farquhar, Jelena Luketina, 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, Wendelin Böhmer, Shimon Whiteson, Fei Sha

CoRR, 2020

Privileged Information Dropout in Reinforcement Learning.

Pierre-Alexandre Kamienny, Kai Arulkumaran, Feryal M. P. Behbahani, Wendelin Boehmer, Shimon Whiteson

CoRR, 2020

Deep Multi-Agent Reinforcement Learning for Decentralized Continuous Cooperative Control.

Christian Schröder de Witt, Bei Peng, Pierre-Alexandre Kamienny, Philip H. S. Torr, Wendelin Böhmer, Shimon Whiteson

CoRR, 2020

Multitask Soft Option Learning.

Maximilian Igl, Andrew Gambardella, Jinke He, Nantas Nardelli, N. Siddharth, Wendelin Boehmer, Shimon Whiteson

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

Deep Coordination Graphs.

Wendelin Boehmer, Vitaly Kurin, Shimon Whiteson

Proceedings of the 37th International Conference on Machine Learning, 2020

Optimistic Exploration even with a Pessimistic Initialisation.

Tabish Rashid, Bei Peng, Wendelin Boehmer, Shimon Whiteson

Proceedings of the 8th International Conference on Learning Representations, 2020

Deep Residual Reinforcement Learning.

Shangtong Zhang, Wendelin Boehmer, Shimon Whiteson

Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, 2020

2019

Exploration with Unreliable Intrinsic Reward in Multi-Agent Reinforcement Learning.

Wendelin Böhmer, Tabish Rashid, Shimon Whiteson

CoRR, 2019

Multitask Soft Option Learning.

Maximilian Igl, Andrew Gambardella, Nantas Nardelli, N. Siddharth, Wendelin Böhmer, Shimon Whiteson

CoRR, 2019

Multi-agent Hierarchical Reinforcement Learning with Dynamic Termination.

Dongge Han, Wendelin Böhmer, Michael J. Wooldridge, Alex Rogers

Proceedings of the PRICAI 2019: Trends in Artificial Intelligence, 2019

Generalized Off-Policy Actor-Critic.

Shangtong Zhang, Wendelin Boehmer, Shimon Whiteson

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Multi-Agent Common Knowledge Reinforcement Learning.

Christian Schröder de Witt, Jakob N. Foerster, Gregory Farquhar, Philip H. S. Torr, Wendelin Boehmer, Shimon Whiteson

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2017

Representation and generalization in autonomous reinforcement learning.

Wendelin Böhmer

PhD thesis, 2017

2016

Non-Deterministic Policy Improvement Stabilizes Approximated Reinforcement Learning.

Wendelin Böhmer, Rong Guo, Klaus Obermayer

CoRR, 2016

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

Regression with Linear Factored Functions.

Wendelin Böhmer, Klaus Obermayer

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2015

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

Factored Representations for Regression.

Wendelin Böhmer, Klaus Obermayer

CoRR, 2014

2013

Construction of approximation spaces for reinforcement learning.

Wendelin Böhmer, Steffen Grünewälder, Yun Shen, Marek Musial, Klaus Obermayer

J. Mach. Learn. Res., 2013

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

Robot Navigation using Reinforcement Learning and Slow Feature Analysis

Wendelin Böhmer

CoRR, 2012

2011

Regularized Sparse Kernel Slow Feature Analysis.

Wendelin Böhmer, Steffen Grünewälder, Hannes Nickisch, Klaus Obermayer

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2011