2022
CoBERL: Contrastive BERT for Reinforcement Learning
Andrea Banino, Adria Puigdomenech Badia, Jacob C Walker, Tim Scholtes, Jovana Mitrovic, Charles Blundell
ICLR 2022spotlight
Many reinforcement learning (RL) agents require a large amount of experience to solve tasks. We propose Contrastive BERT for RL (COBERL), an agent that combines a new contrastive loss and a hybrid LSTM-transformer architecture to tackle the challenge of improving data efficiency. COBERL enables effi…