SMARTS: An Open-Source Scalable Multi-Agent RL Training School for Autonomous Driving. - Ming Zhou 006; Jun Luo 009; Julian Villela; Yaodong Yang 002; David Rusu; Jiayu Miao; Weinan Zhang 001; Montgomery Alban; Iman Fadakar; Zheng Chen; Chongxi Huang; Ying Wen 001; et al. (CoRL, 2020)

SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving. - Ming Zhou 006; Jun Luo 009; Julian Villela; Yaodong Yang 002; David Rusu; Jiayu Miao; Weinan Zhang 001; Montgomery Alban; Iman Fadakar; Zheng Chen; Aurora Chongxi Huang; Ying Wen 001; et al. (CoRR, 2020)

Revisiting Model-based Value Expansion. - Daniel Palenicek; Michael Lutter; Jan Peters 001 (CoRR, 2022)

SAMBA: safe model-based & active reinforcement learning. - Alexander I. Cowen-Rivers; Daniel Palenicek; Vincent Moens; Mohammed Amin Abdullah 001; Aivar Sootla; Jun Wang 012; Haitham Bou-Ammar (Mach. Learn., 2022)

Diminishing Return of Value Expansion Methods in Model-Based Reinforcement Learning. - Daniel Palenicek; Michael Lutter; Joao Carvalho; Jan Peters 001 (ICLR, 2023)

Pseudo-Likelihood Inference. - Theo Gruner; Boris Belousov; Fabio Muratore; Daniel Palenicek; Jan R. Peters (NeurIPS, 2023)

CrossQ: Batch Normalization in Deep Reinforcement Learning for Greater Sample Efficiency and Simplicity. - Aditya Bhatt 001; Daniel Palenicek; Boris Belousov; Max Argus; Artemij Amiranashvili; Thomas Brox; Jan Peters 001 (ICLR, 2024)

Iterated Q-Network: Beyond the One-Step Bellman Operator. - Théo Vincent; Daniel Palenicek; Boris Belousov; Jan Peters 001; Carlo D'Eramo (CoRR, 2024)

Learning Tactile Insertion in the Real World. - Daniel Palenicek; Theo Gruner; Tim Schneider; Alina Böhm; Janis Lenz; Inga Pfenning; Eric Krämer; Jan Peters 001 (CoRR, 2024)

Analysing the Interplay of Vision and Touch for Dexterous Insertion Tasks. - Janis Lenz; Theo Gruner; Daniel Palenicek; Tim Schneider; Jan Peters 001 (CoRR, 2024)

Diminishing Return of Value Expansion Methods. - Daniel Palenicek; Michael Lutter; João Carvalho; Daniel Dennert; Faran Ahmad; Jan Peters 001 (CoRR, 2024)

Iterated Q-Network: Beyond One-Step Bellman Updates in Deep Reinforcement Learning. - Théo Vincent; Daniel Palenicek; Boris Belousov; Jan Peters 001; Carlo D'Eramo (Trans. Mach. Learn. Res., 2025)

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization. - Daniel Palenicek; Florian Vogt; Jan Peters 001 (CoRR, 2025)

Towards Safe Robot Foundation Models. - Maximilian Tölle; Theo Gruner; Daniel Palenicek; Jonas Günster; Puze Liu; Joe Watson; Davide Tateo; Jan Peters 001 (CoRR, 2025)

Gait in Eight: Efficient On-Robot Learning for Omnidirectional Quadruped Locomotion. - Nico Bohlinger; Jonathan Kinzel; Daniel Palenicek; Lukasz Antczak; Jan Peters 001 (CoRR, 2025)

Towards Safe Robot Foundation Models Using Inductive Biases. - Maximilian Tölle; Theo Gruner; Daniel Palenicek; Tim Schneider; Jonas Günster; Joe Watson; Davide Tateo; Puze Liu; Jan Peters 001 (CoRR, 2025)

Scaling CrossQ with Weight Normalization. - Daniel Palenicek; Florian Vogt; Jan Peters 001 (CoRR, 2025)