CoBERL: Contrastive BERT for Reinforcement Learning

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 efficient and robust learning from pixels across a wide variety of domains. We use bidirectional masked prediction in combination with a generalization of a recent contrastive method to learn better representations for RL, without the need of hand engineered data augmentations. We find that COBERL consistently improves data efficiency across the full Atari suite, a set of control tasks and a challenging 3D environment, and often it also increases final score performance.

Authors

• Andrea Banino
• Adrià Puigdomènech Badia
• Jacob C. Walker
• Tim Scholtes
• Jovana Mitrovic
• Charles Blundell

Keywords

• Reinforcement Learning
• Contrastive Learning
• Representation Learning
• Transformer
• Deep Reinforcement Learning