CoBERL: Contrastive BERT for Reinforcement Learning

Andrea Banino, Adria Puigdomenech Badia, Jacob C Walker, Tim Scholtes, Jovana Mitrovic, Charles Blundell

Keywords: Reinforcement Learning, Contrastive Learning, Representation Learning, Transformer, Deep Reinforcement Learning

Abstract: 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.

One-sentence Summary: A new loss and an improved architecture to efficiently train attentional models in reinforcement learning.

Community Implementations: [ 3 code implementations](https://www.catalyzex.com/paper/arxiv:2107.05431/code)