ML Anthology

Palenicek, Daniel

10 publications

ICML 2025 DIME: Diffusion-Based Maximum Entropy Reinforcement Learning Onur Celik, Zechu Li, Denis Blessing, Ge Li, Daniel Palenicek, Jan Peters, Georgia Chalvatzaki, Gerhard Neumann
ICLRW 2025 Diffusion-Based Maximum Entropy Reinforcement Learning Onur Celik, Zechu Li, Denis Blessing, Ge Li, Daniel Palenicek, Jan Peters, Georgia Chalvatzaki, Gerhard Neumann
TMLR 2025 Iterated $q$-Network: Beyond One-Step Bellman Updates in Deep Reinforcement Learning Théo Vincent, Daniel Palenicek, Boris Belousov, Jan Peters, Carlo D'Eramo
NeurIPS 2025 Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization Daniel Palenicek, Florian Vogt, Joe Watson, Jan Peters
ICLR 2024 CrossQ: Batch Normalization in Deep Reinforcement Learning for Greater Sample Efficiency and Simplicity Aditya Bhatt, Daniel Palenicek, Boris Belousov, Max Argus, Artemij Amiranashvili, Thomas Brox, Jan Peters
ICLR 2023 Diminishing Return of Value Expansion Methods in Model-Based Reinforcement Learning Daniel Palenicek, Michael Lutter, Joao Carvalho, Jan Peters
NeurIPS 2023 Pseudo-Likelihood Inference Theo Gruner, Boris Belousov, Fabio Muratore, Daniel Palenicek, Jan R Peters
ICLRW 2022 Revisiting Model-Based Value Expansion Daniel Palenicek, Michael Lutter, Jan Peters
MLJ 2022 SAMBA: Safe Model-Based & Active Reinforcement Learning Alexander I. Cowen-Rivers, Daniel Palenicek, Vincent Moens, Mohammed Amin Abdullah, Aivar Sootla, Jun Wang, Haitham Bou-Ammar
CoRL 2020 SMARTS: An Open-Source Scalable Multi-Agent RL Training School for Autonomous Driving Ming Zhou, Jun Luo, Julian Villella, Yaodong Yang, David Rusu, Jiayu Miao, Weinan Zhang, Montgomery Alban, Iman Fadakar, Zheng Chen, Chongxi Huang, Ying Wen, Kimia Hassanzadeh, Daniel Graves, Zhengbang Zhu, Yihan Ni, Nhat Nguyen, Mohamed Elsayed, Haitham Ammar, Alexander Cowen-Rivers, Sanjeevan Ahilan, Zheng Tian, Daniel Palenicek, Kasra Rezaee, Peyman Yadmellat, Kun Shao, Dong Chen, Baokuan Zhang, Hongbo Zhang, Jianye Hao, Wulong Liu, Jun Wang