Aidan Scannell

According to our database, Aidan Scannell authored at least 9 papers between 2021 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2025

Generalist World Model Pre-Training for Efficient Reinforcement Learning.

Yi Zhao, Aidan Scannell, Yuxin Hou, Tianyu Cui, Le Chen, Dieter Büchler, Arno Solin, Juho Kannala, Joni Pajarinen

CoRR, February, 2025

Discrete Codebook World Models for Continuous Control.

Aidan Scannell, Mohammadreza Nakhaeinezhadfard, Kalle Kujanpää, Yi Zhao, Kevin Sebastian Luck, Arno Solin, Joni Pajarinen

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Entropy Regularized Task Representation Learning for Offline Meta-Reinforcement Learning.

Mohammadreza Nakhaeinezhadfard, Aidan Scannell, Joni Pajarinen

Proceedings of the AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25, 2025

2024

iQRL - Implicitly Quantized Representations for Sample-efficient Reinforcement Learning.

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

CoRR, 2024

Residual learning and context encoding for adaptive offline-to-online reinforcement learning.

Mohammadreza Nakhaei, Aidan Scannell, Joni Pajarinen

Proceedings of the 6th Annual Learning for Dynamics & Control Conference, 2024

Function-space Parameterization of Neural Networks for Sequential Learning.

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

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Sparse Function-space Representation of Neural Networks.

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

CoRR, 2023

Mode-constrained Model-based Reinforcement Learning via Gaussian Processes.

Aidan Scannell, Carl Henrik Ek, Arthur Richards

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2021

Trajectory Optimisation in Learned Multimodal Dynamical Systems via Latent-ODE Collocation.

Aidan Scannell, Carl Henrik Ek, Arthur Richards

Proceedings of the IEEE International Conference on Robotics and Automation, 2021