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Leveraging Unlabeled Offline Data via Model-based Active Semi-supervised Reinforcement Learning
Yi Zhao, Aidan Scannell, Tianyu Cui, Le Chen, Arno Solin, Juho Kannala, Joni Pajarinen
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iQRL - Implicitly Quantized Representations for Sample-efficient Reinforcement Learning
Learning representations for reinforcement learning (RL) has shown much promise for continuous control. We propose an efficient …
Aidan Scannell, Kalle Kujanpää, Yi Zhao, Mohammadreza Nakhaei, Arno Solin, Joni Pajarinen
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iQRL - Implicitly Quantized Representations for Sample-efficient Reinforcement Learning
Quantized Representations Prevent Dimensional Collapse in Self-predictive RL
Learning representations for reinforcement learning (RL) has shown much promise for continuous control. We propose an efficient …
Aidan Scannell, Kalle Kujanpää, Yi Zhao, Mohammadreza Nakhaei, Arno Solin, Joni Pajarinen
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Sparse Function-space Representation of Neural Networks
Deep neural networks (NNs) are known to lack uncertainty estimates and struggle to incorporate new data. We present a method that …
Aidan Scannell, Riccardo Mereu, Paul Chang, Ella Tamir, Joni Pajarinen, Arno Solin
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Sparse Function-space Representation of Neural Networks
Identifiable Mixtures of Sparse Variational Gaussian Process Experts
Mixture models are inherently unidentifiable as different combinations of component distributions and mixture weights can generate the …
Aidan Scannell, Carl Henrik Ek, Arthur Richards
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