Wendelin Böhmer

发表

Construction of approximation spaces for reinforcement learning

Klaus Obermayer, Wendelin Böhmer, Marek Musial, 2013, J. Mach. Learn. Res..

Towards Structural Generalization : Factored Approximate Planning

K. Obermayer, Wendelin Böhmer, 2013 .

Optimistic Exploration even with a Pessimistic Initialisation pdf

Shimon Whiteson, Wendelin Böhmer, Bei Peng, 2020, ICLR.

Regularized Sparse Kernel Slow Feature Analysis

Klaus Obermayer, Wendelin Böhmer, Hannes Nickisch, 2011, ECML/PKDD.

Deep Multi-Agent Reinforcement Learning for Decentralized Continuous Cooperative Control

Philip H. S. Torr, S. Whiteson, Wendelin Böhmer, 2020, ArXiv.

Deep Coordination Graphs

S. Whiteson, Wendelin Böhmer, Vitaly Kurin, 2019, ICML.

The effect of novelty on reinforcement learning.

K. Obermayer, Wendelin Böhmer, R. Lorenz, 2013, Progress in brain research.

Interaction of Instrumental and Goal-Directed Learning Modulates Prediction Error Representations in the Ventral Striatum

K. Obermayer, J. Gläscher, Wendelin Böhmer, 2016, The Journal of Neuroscience.

State Extraction by Dimensionality Reduction

K. Obermayer, Wendelin Böhmer, 2004 .

Surrogate DC Microgrid Models for Optimization of Charging Electric Vehicles under Partial Observability

Mathijs M. de Weerdt, Wendelin Böhmer, L. Mackay, 2022, Energies.

A Simplified Three-Dimensional Ice-Sheet Model Including Ice Shelves

Wendelin Böhmer, K. Herterich, 1990, Annals of Glaciology.

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

M. Spaan, Wendelin Böhmer, Yaniv Oren, 2022, ArXiv.

Multi-Agent Common Knowledge Reinforcement Learning

Shimon Whiteson, Philip H. S. Torr, Jakob N. Foerster, 2018, NeurIPS.

Representation and generalization in autonomous reinforcement learning

Wendelin Böhmer, Wendelin Böhmer, 2017 .

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

Shimon Whiteson, Wendelin Böhmer, Tabish Rashid, 2019, ArXiv.

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

Klaus Obermayer, Wendelin Böhmer, Martin A. Riedmiller, 2015, KI - Künstliche Intelligenz.

Deep Residual Reinforcement Learning

Shimon Whiteson, Wendelin Boehmer, Shangtong Zhang, 2019, AAMAS.

UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning

Wendelin Böhmer, Bei Peng, Shimon Whiteson, 2020, ICML.

Diverse Projection Ensembles for Distributional Reinforcement Learning

Moritz A. Zanger, M. Spaan, Wendelin Böhmer, 2023, ArXiv.

Multi-Agent Hierarchical Reinforcement Learning with Dynamic Termination pdf

Michael Wooldridge, Alex Rogers, Wendelin Böhmer, 2019, AAMAS.

Neural systems for choice and valuation with counterfactual learning signals

Christian Büchel, Klaus Obermayer, Wendelin Böhmer, 2014, NeuroImage.

Supplementary Material: Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning

Fei Sha, Wendelin Böhmer, Bei Peng, 2021 .

Multitask Soft Option Learning

Shimon Whiteson, Wendelin Böhmer, N. Siddharth, 2019, UAI.

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

Javier Alonso-Mora, Wendelin Böhmer, Hai Zhu, 2023, Autonomous Robots.

Non-Deterministic Policy Improvement Stabilizes Approximated Reinforcement Learning pdf

Klaus Obermayer, Wendelin Böhmer, Rong Guo, 2016, ArXiv.

The effect of novelty on reinforcement learning.

K. Obermayer, Wendelin Böhmer, R. Lorenz, 2013, Progress in brain research.

The Role of Diverse Replay for Generalisation in Reinforcement Learning

M. Spaan, Wendelin Böhmer, Max Weltevrede, 2023, ArXiv.

Regression with Linear Factored Functions

K. Obermayer, Wendelin Böhmer, 2014, ECML/PKDD.

Active Classification of Moving Targets With Learned Control Policies

Javier Alonso-Mora, Wendelin Böhmer, E. Montijano, 2022, IEEE Robotics and Automation Letters.

Factored Representations for Regression

Klaus Obermayer, Wendelin Böhmer, K. Obermayer, 2014, ArXiv.

Generating feature spaces for linear algorithms with regularized sparse kernel slow feature analysis

Klaus Obermayer, Wendelin Böhmer, Hannes Nickisch, 2012, Machine Learning.