self-supervised-learning

iQRL: Implicitly Quantized Representations for Sample-Efficient Reinforcement Learning
I will be presenting our research on self-supervised representation learning for reinforcement learning at the International Workshop of Intelligent Autonomous Learning Systems 2024.
Jul 23, 2024 2:50 PM — 3:05 PM Darmstädter Haus and Sporthotel Walliser, Kleinwalsertal, Austria
Aidan Scannell
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iQRL: Implicitly Quantized Representations for Sample-Efficient Reinforcement Learning
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
Cite Website
Implicitly Quantized Representations for Reinforcement Learning
Learning representations for reinforcement learning (RL) has shown much promise for continuous control. In this project, we investigate using vector quantization to prevent representation collapse when learning representations for RL using a self-supervised latent-state consistency loss.
Aidan Scannell, Kalle Kujanpää, Yi Zhao, Mohammadreza Nakhaei, Arno Solin, Joni Pajarinen
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Implicitly Quantized Representations for Reinforcement Learning