Source Themes

iQRL – Implicitly Quantized Representations for Sample-efficient Reinforcement Learning

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

Function-space Prameterization of Neural Networks for Sequential Learning

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

Optimistic Multi-Agent Policy Gradient

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

Probabilistic Subgoal Representations for Hierarchical Reinforcement learning

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

Quantized Representations Prevent Dimensional Collapse in Self-predictive RL

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

Residual Learning and Context Encoding for Adaptive Offline-to-Online Reinforcement Learning

Mohammadreza Nakhaei, Aidan Scannell, Joni Pajarinen

Hybrid search for efficient planning with completeness guarantees

Kalle Kujanpää, Joni Pajarinen, Alexander Ilin

Hierarchical Imitation Learning with Vector Quantized Models

Kalle Kujanpää, Joni Pajarinen, Alexander Ilin

Simplified Temporal Consistency Reinforcement Learning

Yi Zhao, Wenshuai Zhao, Rinu Boney, Juho Kannala, Joni Pajarinen

Sparse Function-space Representation of Neural Networks

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