Source Themes

On the benefit of optimal transport for curriculum reinforcement learning

Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen

Backpropagation Through Agents

Zhiyuan Li, Wenshuai Zhao, Lijun Wu, Joni Pajarinen, Joni Pajarinen

Optimistic Multi-Agent Policy Gradient

Wenshuai Zhao, Yi Zhao, Zhiyuan Li, Juho Kannala, Joni Pajarinen

RP1M: A Large-Scale Motion Dataset for Piano Playing with Bi-Manual Dexterous Robot Hands

Yi Zhao, Le Chen, Jan Schneider, Quankai Gao, Juho Kannala, Bernhard Schölkopf, Joni Pajarinen, Dieter Büchler

Continuous Monte Carlo Graph Search

Kalle Kujanpää, Amin Babadi, Yi Zhao, Juho Kannala, Alexander Ilin, Joni Pajarinen

Function-space Prameterization of Neural Networks for Sequential Learning

Aidan Scannell, Riccardo Mereu, Paul Chang, Ella Tamir, Joni Pajarinen, Arno Solin

iQRL – Implicitly Quantized Representations for Sample-efficient Reinforcement Learning

Aidan Scannell, Kalle Kujanpää, Yi Zhao, Mohammadreza Nakhaei, Arno Solin, Joni Pajarinen

Probabilistic Subgoal Representations for Hierarchical Reinforcement learning

Vivienne Huiling Wang, Tinghuai Wang, Wenyan Yang, Joni-Kristian Kämäräinen, Joni Pajarinen

ROER: Regularized Optimal Experience Replay

Changling Li, Zhang-Wei Hong, Pulkit Agrawal, Divyansh Garg, Joni Pajarinen

Quantized Representations Prevent Dimensional Collapse in Self-predictive RL

Aidan Scannell, Kalle Kujanpää, Yi Zhao, Mohammadreza Nakhaei, Arno Solin, Joni Pajarinen