Pretraining Representations for Data-Efficient Reinforcement Learning (2106.04799v1)

Abstract: Data efficiency is a key challenge for deep reinforcement learning. We address this problem by using unlabeled data to pretrain an encoder which is then finetuned on a small amount of task-specific data. To encourage learning representations which capture diverse aspects of the underlying MDP, we employ a combination of latent dynamics modelling and unsupervised goal-conditioned RL. When limited to 100k steps of interaction on Atari games (equivalent to two hours of human experience), our approach significantly surpasses prior work combining offline representation pretraining with task-specific finetuning, and compares favourably with other pretraining methods that require orders of magnitude more data. Our approach shows particular promise when combined with larger models as well as more diverse, task-aligned observational data -- approaching human-level performance and data-efficiency on Atari in our best setting. We provide code associated with this work at https://github.com/mila-iqia/SGI.

• The paper introduces SGI, a novel pretraining method merging latent dynamics, unsupervised goal-conditioned RL, and inverse dynamics modeling to surpass single-objective learning.
• The method achieves human-level performance on the data-constrained Atari 100k benchmark, significantly reducing interaction requirements.
• The results demonstrate that higher-quality data and larger models further scale the benefits, emphasizing its potential in real-world RL tasks.

Pretraining Representations for Data-Efficient Reinforcement Learning: An Expert Overview

The paper, "Pretraining Representations for Data-Efficient Reinforcement Learning," addresses the critical challenge of data efficiency in reinforcement learning (RL) by utilizing unlabeled data for pretraining, followed by finetuning on a small dataset to achieve high efficiency. The authors introduce a novel approach that merges self-supervised learning (SSL) with RL tasks to markedly improve performance while significantly reducing data requirements.

Core Contributions

The central contributions of this research are as follows:

1. Representation Learning with Diverse Objectives: The authors propose a novel pretraining technique called SGI, which includes a combination of latent dynamics modeling, unsupervised goal-conditioned RL, and inverse dynamics modeling. This approach captures multiple aspects of the Markov Decision Process (MDP) rather than relying on a monolithic learning objective. The combination of these three objectives is shown to outperform any single objective.
2. Data Efficiency in Atari 100k: SGI achieves strong performance on the demanding Atari 100k benchmark, where agents are limited to 100,000 steps of interaction, highlighting its data efficiency. In particular, SGI demonstrates the ability to achieve performance levels similar to human players with limited data, performing favorably compared to prior methods, including those requiring significantly more data for pretraining.
3. Scalability with Data Quality and Model Size: The research indicates that the performance of SGI increases with higher data quality and larger model sizes. This scalability is important for real-world applications where high-quality data collection may be feasible, and utilizing larger models can provide improved performance.

Experimental Insights and Implications

The experiments carried out illustrate several key findings:

• Impact of Pretraining Data Quality: The paper highlights the correlation between the quality of pretraining data and the efficacy of subsequent finetuning. While SGI can operate with a variety of data qualities, its performance scales positively with higher-quality datasets, providing a robust strategy for securing efficiency gains.
• Behavioral Cloning as a Baseline: The paper also juxtaposes SGI with a behavioral cloning (BC) baseline under the Mixed dataset scenario, revealing that while BC is competitive, SGI benefits more robustly from the diverse objectives provided by its pretraining scheme.
• Model Size and Pretraining Benefits: SGI showcases that larger models result in better performance when pretrained. Larger networks, while traditionally unstable to finetune, gain stability and improved efficiency through SGI's pretraining.

Theoretical and Practical Implications

The SGI method establishes a strong precedent for using self-supervised pretraining in RL, especially in settings where data acquisition is limited or costly. The implication is that employing a multi-objective approach to pretraining can lead to better generalization in RL tasks, enhancing data efficiency and potentially lowering the barriers to applying RL in complex, real-world scenarios.

Theoretically, this work advances the understanding of representational transfer in RL by demonstrating the empirical benefits of diverse objectives over single-task learning strategies. It underscores the importance of maintaining representation diversity and discusses representational collapse as a factor potentially mitigated by inverse dynamics modeling.

Future Directions

This paper opens multiple avenues for future research, such as exploring the impact of using large-scale, unsupervised exploration datasets for pretraining and examining domain-specific applications where RL tasks have rich, yet untapped, observational data. Enhancements in the stability and scalability of RL through self-supervised objectives will be crucial as applications reach increasing levels of sophistication and complexity.

In conclusion, the paper makes a substantive contribution to the RL field by integrating effective pretraining strategies with a focus on data efficiency, potentially reshaping how representations are learned and leveraged in reinforcement learning paradigms.