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Wendelin Böhmer
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2020 – today
- 2024
- [j7]Sara Casao
, Álvaro Serra-Gómez, Ana C. Murillo, Wendelin Böhmer
Distributed multi-target tracking and active perception with mobile camera networks. Comput. Vis. Image Underst. 238: 103876 (2024) - [c19]Moritz Akiya Zanger, Wendelin Boehmer, Matthijs T. J. Spaan:
Diverse Projection Ensembles for Distributional Reinforcement Learning. ICLR 2024 - [c18]Grigorii Veviurko, Wendelin Boehmer, Mathijs de Weerdt:
To the Max: Reinventing Reward in Reinforcement Learning. ICML 2024 - [i30]Grigorii Veviurko, Wendelin Böhmer, Mathijs de Weerdt:
To the Max: Reinventing Reward in Reinforcement Learning. CoRR abs/2402.01361 (2024) - [i29]Ksenija Stepanovic, Wendelin Böhmer, Mathijs de Weerdt:
A Penalty-Based Guardrail Algorithm for Non-Decreasing Optimization with Inequality Constraints. CoRR abs/2405.01984 (2024) - [i28]Yaniv Oren, Moritz A. Zanger, Pascal R. van der Vaart, Matthijs T. J. Spaan, Wendelin Böhmer:
Value Improved Actor Critic Algorithms. CoRR abs/2406.01423 (2024) - [i27]Max Weltevrede, Felix Kaubek, Matthijs T. J. Spaan, Wendelin Böhmer:
Explore-Go: Leveraging Exploration for Generalisation in Deep Reinforcement Learning. CoRR abs/2406.08069 (2024) - [i26]Laurens Engwegen, Daan Brinks, Wendelin Böhmer:
Generalisation to unseen topologies: Towards control of biological neural network activity. CoRR abs/2407.12789 (2024) - [i25]Max Weltevrede, Caroline Horsch, Matthijs T. J. Spaan, Wendelin Böhmer:
Training on more Reachable Tasks for Generalisation in Reinforcement Learning. CoRR abs/2410.03565 (2024) - 2023
- [j6]Álvaro Serra-Gómez, Hai Zhu, Bruno Brito, Wendelin Böhmer
Learning scalable and efficient communication policies for multi-robot collision avoidance. Auton. Robots 47(8): 1275-1297 (2023) - [j5]Álvaro Serra-Gómez
Active Classification of Moving Targets With Learned Control Policies. IEEE Robotics Autom. Lett. 8(6): 3717-3724 (2023) - [c17]Saray Bakker, Luzia Knödler, Max Spahn, Wendelin Böhmer
Multi-Robot Local Motion Planning Using Dynamic Optimization Fabrics. MRS 2023: 149-155 - [i24]Max Weltevrede, Matthijs T. J. Spaan, Wendelin Böhmer:
The Role of Diverse Replay for Generalisation in Reinforcement Learning. CoRR abs/2306.05727 (2023) - [i23]Moritz A. Zanger, Wendelin Böhmer, Matthijs T. J. Spaan:
Diverse Projection Ensembles for Distributional Reinforcement Learning. CoRR abs/2306.07124 (2023) - [i22]Grigorii Veviurko, Wendelin Böhmer, Mathijs de Weerdt:
You Shall not Pass: the Zero-Gradient Problem in Predict and Optimize for Convex Optimization. CoRR abs/2307.16304 (2023) - [i21]Saray Bakker, Luzia Knödler, Max Spahn, Wendelin Böhmer, Javier Alonso-Mora:
Multi-Robot Local Motion Planning Using Dynamic Optimization Fabrics. CoRR abs/2310.12816 (2023) - [i20]Nathan Ordonez, Marije Tromp, Pau Marquez Julbe, Wendelin Böhmer:
Lights out: training RL agents robust to temporary blindness. CoRR abs/2312.02665 (2023) - 2022
- [i19]Yaniv Oren, Matthijs T. J. Spaan, Wendelin Böhmer
Planning with Uncertainty: Deep Exploration in Model-Based Reinforcement Learning. CoRR abs/2210.13455 (2022) - [i18]Álvaro Serra-Gómez, Eduardo Montijano, Wendelin Böhmer
Active Classification of Moving Targets with Learned Control Policies. CoRR abs/2212.03068 (2022) - 2021
- [c16]Maximilian Igl, Gregory Farquhar, Jelena Luketina, Wendelin Boehmer, Shimon Whiteson:
Transient Non-stationarity and Generalisation in Deep Reinforcement Learning. ICLR 2021 - [c15]Vitaly Kurin, Maximilian Igl, Tim Rocktäschel, Wendelin Boehmer, Shimon Whiteson:
My Body is a Cage: the Role of Morphology in Graph-Based Incompatible Control. ICLR 2021 - [c14]Tarun Gupta, Anuj Mahajan, Bei Peng, Wendelin Boehmer, Shimon Whiteson:
UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning. ICML 2021: 3930-3941 - [c13]Shariq Iqbal, Christian A. Schröder de Witt, Bei Peng, Wendelin Boehmer, Shimon Whiteson, Fei Sha:
Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning. ICML 2021: 4596-4606 - [c12]Shangtong Zhang, Wendelin Boehmer, Shimon Whiteson:
Deep Residual Reinforcement Learning (Extended Abstract). IJCAI 2021: 4869-4873 - [c11]Bei Peng, Tabish Rashid, Christian Schröder de Witt, Pierre-Alexandre Kamienny, Philip H. S. Torr, Wendelin Boehmer, Shimon Whiteson:
FACMAC: Factored Multi-Agent Centralised Policy Gradients. NeurIPS 2021: 12208-12221 - 2020
- [c10]Shangtong Zhang, Wendelin Boehmer, Shimon Whiteson:
Deep Residual Reinforcement Learning. AAMAS 2020: 1611-1619 - [c9]Tabish Rashid, Bei Peng, Wendelin Boehmer, Shimon Whiteson:
Optimistic Exploration even with a Pessimistic Initialisation. ICLR 2020 - [c8]Wendelin Boehmer, Vitaly Kurin, Shimon Whiteson:
Deep Coordination Graphs. ICML 2020: 980-991 - [c7]Maximilian Igl, Andrew Gambardella, Jinke He, Nantas Nardelli, N. Siddharth, Wendelin Boehmer, Shimon Whiteson:
Multitask Soft Option Learning. UAI 2020: 969-978 - [i17]Tabish Rashid, Bei Peng, Wendelin Böhmer, Shimon Whiteson:
Optimistic Exploration even with a Pessimistic Initialisation. CoRR abs/2002.12174 (2020) - [i16]Christian Schröder de Witt, Bei Peng, Pierre-Alexandre Kamienny, Philip H. S. Torr, Wendelin Böhmer, Shimon Whiteson:
Deep Multi-Agent Reinforcement Learning for Decentralized Continuous Cooperative Control. CoRR abs/2003.06709 (2020) - [i15]Pierre-Alexandre Kamienny, Kai Arulkumaran, Feryal M. P. Behbahani, Wendelin Boehmer, Shimon Whiteson:
Privileged Information Dropout in Reinforcement Learning. CoRR abs/2005.09220 (2020) - [i14]Shariq Iqbal, Christian A. Schröder de Witt, Bei Peng, Wendelin Böhmer, Shimon Whiteson, Fei Sha:
AI-QMIX: Attention and Imagination for Dynamic Multi-Agent Reinforcement Learning. CoRR abs/2006.04222 (2020) - [i13]Maximilian Igl, Gregory Farquhar, Jelena Luketina, Wendelin Boehmer, Shimon Whiteson:
The Impact of Non-stationarity on Generalisation in Deep Reinforcement Learning. CoRR abs/2006.05826 (2020) - [i12]Vitaly Kurin, Maximilian Igl, Tim Rocktäschel, Wendelin Boehmer, Shimon Whiteson:
My Body is a Cage: the Role of Morphology in Graph-Based Incompatible Control. CoRR abs/2010.01856 (2020) - [i11]Tarun Gupta, Anuj Mahajan, Bei Peng, Wendelin Böhmer, Shimon Whiteson:
UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning. CoRR abs/2010.02974 (2020)
2010 – 2019
- 2019
- [c6]Dongge Han, Wendelin Boehmer, Michael J. Wooldridge, Alex Rogers:
Multi-Agent Hierarchical Reinforcement Learning with Dynamic Termination. AAMAS 2019: 2006-2008 - [c5]Shangtong Zhang, Wendelin Boehmer, Shimon Whiteson:
Generalized Off-Policy Actor-Critic. NeurIPS 2019: 1999-2009 - [c4]Christian Schröder de Witt, Jakob N. Foerster, Gregory Farquhar, Philip H. S. Torr, Wendelin Boehmer, Shimon Whiteson:
Multi-Agent Common Knowledge Reinforcement Learning. NeurIPS 2019: 9924-9935 - [c3]Dongge Han, Wendelin Böhmer
Multi-agent Hierarchical Reinforcement Learning with Dynamic Termination. PRICAI (2) 2019: 80-92 - [i10]Shangtong Zhang, Wendelin Boehmer, Shimon Whiteson:
Generalized Off-Policy Actor-Critic. CoRR abs/1903.11329 (2019) - [i9]Maximilian Igl, Andrew Gambardella, Nantas Nardelli, N. Siddharth, Wendelin Böhmer, Shimon Whiteson:
Multitask Soft Option Learning. CoRR abs/1904.01033 (2019) - [i8]Shangtong Zhang, Wendelin Boehmer, Shimon Whiteson:
Deep Residual Reinforcement Learning. CoRR abs/1905.01072 (2019) - [i7]Wendelin Böhmer, Tabish Rashid, Shimon Whiteson:
Exploration with Unreliable Intrinsic Reward in Multi-Agent Reinforcement Learning. CoRR abs/1906.02138 (2019) - [i6]Wendelin Böhmer, Vitaly Kurin, Shimon Whiteson:
Deep Coordination Graphs. CoRR abs/1910.00091 (2019) - [i5]Dongge Han, Wendelin Boehmer, Michael J. Wooldridge, Alex Rogers:
Multi-agent Hierarchical Reinforcement Learning with Dynamic Termination. CoRR abs/1910.09508 (2019) - 2018
- [i4]Jakob N. Foerster, Christian A. Schröder de Witt, Gregory Farquhar, Philip H. S. Torr, Wendelin Boehmer, Shimon Whiteson:
Multi-Agent Common Knowledge Reinforcement Learning. CoRR abs/1810.11702 (2018) - 2017
- [b1]Wendelin Böhmer:
Representation and generalization in autonomous reinforcement learning. Technical University of Berlin, Germany, 2017 - 2016
- [i3]Wendelin Böhmer, Rong Guo, Klaus Obermayer:
Non-Deterministic Policy Improvement Stabilizes Approximated Reinforcement Learning. CoRR abs/1612.07548 (2016) - 2015
- [j4]Wendelin Böhmer
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. Künstliche Intell. 29(4): 353-362 (2015) - [c2]Wendelin Böhmer
Regression with Linear Factored Functions. ECML/PKDD (1) 2015: 119-134 - 2014
- [j3]Michael J. Tobia
Neural systems for choice and valuation with counterfactual learning signals. NeuroImage 89: 57-69 (2014) - [i2]Wendelin Böhmer, Klaus Obermayer:
Factored Representations for Regression. CoRR abs/1412.6286 (2014) - 2013
- [j2]Wendelin Böhmer, Steffen Grünewälder, Yun Shen, Marek Musial, Klaus Obermayer:
Construction of approximation spaces for reinforcement learning. J. Mach. Learn. Res. 14(1): 2067-2118 (2013) - 2012
- [j1]Wendelin Böhmer
Generating feature spaces for linear algorithms with regularized sparse kernel slow feature analysis. Mach. Learn. 89(1-2): 67-86 (2012) - [i1]Wendelin Böhmer:
Robot Navigation using Reinforcement Learning and Slow Feature Analysis. CoRR abs/1205.0986 (2012) - 2011
- [c1]Wendelin Böhmer
Regularized Sparse Kernel Slow Feature Analysis. ECML/PKDD (1) 2011: 235-248
Coauthor Index
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