Decoupling Representation Learning from Reinforcement Learning

Adam Stooke, Kimin Lee, Pieter Abbeel, Michael Laskin

Keywords: reinforcement learning, representation learning, unsupervised learning

Abstract: In an effort to overcome limitations of reward-driven feature learning in deep reinforcement learning (RL) from images, we propose decoupling representation learning from policy learning. To this end, we introduce a new unsupervised learning (UL) task, called Augmented Temporal Contrast (ATC), which trains a convolutional encoder to associate pairs of observations separated by a short time difference, under image augmentations and using a contrastive loss. In online RL experiments, we show that training the encoder exclusively using ATC matches or outperforms end-to-end RL in most environments. Additionally, we benchmark several leading UL algorithms by pre-training encoders on expert demonstrations and using them, with weights frozen, in RL agents; we find that agents using ATC-trained encoders outperform all others. We also train multi-task encoders on data from multiple environments and show generalization to different downstream RL tasks. Finally, we ablate components of ATC, and introduce a new data augmentation to enable replay of (compressed) latent images from pre-trained encoders when RL requires augmentation. Our experiments span visually diverse RL benchmarks in DeepMind Control, DeepMind Lab, and Atari, and our complete code is available at \url{hidden url}.

One-sentence Summary: We introduce a new unsupervised learning task tailored for RL that, for the first time, supports representation learning fully decoupled from policy learning, as demonstrated across a range of visually diverse RL benchmarks.

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Community Implementations: [ 3 code implementations](https://www.catalyzex.com/paper/arxiv:2009.08319/code)

Reviewed Version (pdf): https://openreview.net/references/pdf?id=4PhfAS0KP