Implicitly Quantized Representations for Reinforcement Learning

Aidan Scannell, Kalle Kujanpää, Yi Zhao, Arno Solin, Joni Pajarinen
Mar 24, 2024
Code

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.

reinforcement-learning machine-learning representation-learning robotics python pytorch research