Wendelin Böhmer

FACMAC: Factored Multi-Agent Centralised Policy Gradients

Bei Peng, Tabish Rashid, Christian A. Schroeder de Witt et al. · arXiv (Cornell University) · 2020 · 106 citations · Full text

Mathematical Programming, Artificial Intelligence, Policy Gradients +15

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 et al. · KI - Künstliche Intelligenz · 2015 · 51 citations

Artificial Intelligence, Engineering, Machine Learning +9

Deep Coordination Graphs

Wendelin Böhmer, Vitaly Kurin, Shimon Whiteson · arXiv (Cornell University) · 2019 · 42 citations · Full text

Artificial Intelligence, Engineering, Machine Learning +20

Deep Multi-Agent Reinforcement Learning for Decentralized Continuous Cooperative Control

Christian Schröder de Witt, Bei Peng, Pierre-Alexandre Kamienny et al. · arXiv (Cornell University) · 2020 · 41 citations · Full text

Artificial Intelligence, Multi-agent Reinforcement Learning, Machine Learning +12

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

Wendelin Böhmer, Steffen Grünewälder, Hannes Nickisch et al. · Machine Learning · 2012 · 23 citations · Full text

Sparse Representation, Image Analysis, Machine Learning +10