Decoupling Representation Learning from Reinforcement Learning (2009.08319v3)

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 https://github.com/astooke/rlpyt/tree/master/rlpyt/ul.

• The paper introduces Augmented Temporal Contrast (ATC) to decouple representation learning from reward signals in RL using unsupervised temporal contrast and data augmentation.
• ATC-trained encoders match or exceed end-to-end RL performance, notably improving results in sparse reward environments and via pre-training.
• Decoupling enables learning reward-agnostic representations for better multi-task generalization and sample efficiency in new RL tasks.

Decoupling Representation Learning from Reinforcement Learning

The paper "Decoupling Representation Learning from Reinforcement Learning" by Adam Stooke, Kimin Lee, Pieter Abbeel, and Michael Laskin focuses on the integration of representation learning into reinforcement learning (RL) systems without relying heavily on reward signals. This research introduces the Augmented Temporal Contrast (ATC) as a novel approach to learning representations in an unsupervised manner. Unlike traditional methods that jointly learn visual features and control policies, ATC decouples representation learning from policy learning, thereby addressing shortcomings posed by sparse reward environments.

The ATC framework comprises a convolutional encoder that associates temporal pairs of observations subjected to stochastic data augmentation. Specifically, ATC utilizes a contrastive loss over short time frames to enhance representational quality. This method demonstrates superior or comparable performance to end-to-end RL methods in visually varied benchmarks such as DeepMind Control, DeepMind Lab, and Atari games. Notably, ATC excels in environments where traditional RL algorithms struggle with sparse rewards.

The paper details several key contributions and results:

1. Online RL Performance: The ATC-trained encoder, decoupled from the RL gradient updates, matches or exceeds the performance of traditional end-to-end RL methods in several test environments, including DMControl and DMLab. In scenarios with sparse rewards, ATC notably enhances performance.
2. Encoder Pre-Training Benchmarks: By pre-training encoders solely on expert demonstrations and freezing weights during the RL phase, ATC outperforms leading unsupervised learning algorithms such as those involved in state-of-the-art auxiliary tasks. This benchmarks the efficacy of ATC in generating rewarding encoder structures for diverse RL environments.
3. Multi-Task Generalization: ATC demonstrates the potential for efficient multi-task representation learning through simultaneous encoder pre-training on multiple environments. The paper shows promising results in cross-domain generalization, notably improving sample efficiency in new RL tasks.
4. Impact of Data Augmentation: The inclusion of random shift augmentation is vital across environments, ensuring encoder robustness. The research introduces subpixel random shift to realize computation and memory efficiencies by focusing data augmentation on latent images in DMControl.
5. Ablation Studies: The paper includes ablations assessing the effect of ATC components, underscoring the significance of temporal contrast and data augmentation in enhancing performance in environments like DMLab's Lasertag.

Theoretical implications of this research reach beyond practical performance gains. By dissociating representation learning from the reward signal dependencies, the paper enriches the understanding of predictive representations in unsupervised settings. This approach opens the door to leveraging reward-agnostic encoders for generalized policy learning tasks. It also suggests unexplored intersections between model-free and model-based reinforcement learning paradigms, potentially guiding future advancements in latent space world-modeling and environment simulation.

Despite ATC's capability, the paper acknowledges that further research is warranted to fully harness the decoupling strategy across broader RL domains, particularly for complex scenarios encountered in certain Atari environments. Future directions may involve exploring additional unsupervised learning methodologies, leveraging structured representations from even more dynamic and diverse datasets, and extending such algorithms to more intricate real-world tasks.