Value Explicit Pretraining for Learning Transferable Representations

Abstract

We propose Value Explicit Pretraining (VEP), a method that learns generalizable representations for transfer reinforcement learning. VEP enables learning of new tasks that share similar objectives as previously learned tasks, by learning an encoder for objective-conditioned representations, irrespective of appearance changes and environment dynamics. To pre-train the encoder from a sequence of observations, we use a self-supervised contrastive loss that results in learning temporally smooth representations. VEP learns to relate states across different tasks based on the Bellman return estimate that is reflective of task progress. Experiments using a realistic navigation simulator and Atari benchmark show that the pretrained encoder produced by our method outperforms current SoTA pretraining methods on the ability to generalize to unseen tasks. VEP achieves up to a 2 times improvement in rewards on Atari and visual navigation, and up to a 3 times improvement in sample efficiency. For videos of policy performance visit our https://sites.google.com/view/value-explicit-pretraining/