Learning Representations for Pixel-based Control: What Matters and Why?

TL;DR

This paper investigates the challenges of learning pixel-based control representations in complex environments with distractors, proposing a simple baseline and analyzing the limitations of existing methods to improve real-world RL applications.

Contribution

It introduces a straightforward baseline for pixel control in challenging settings and provides a detailed analysis of when and why existing methods fail, emphasizing the importance of benchmark characteristics.

Findings

Baseline approach learns meaningful representations without complex techniques.

Existing methods often fail or perform similarly to the baseline in distractor-rich environments.

Benchmark evaluation should consider environment characteristics like reward density and distractors.

Abstract

Learning representations for pixel-based control has garnered significant attention recently in reinforcement learning. A wide range of methods have been proposed to enable efficient learning, leading to sample complexities similar to those in the full state setting. However, moving beyond carefully curated pixel data sets (centered crop, appropriate lighting, clear background, etc.) remains challenging. In this paper, we adopt a more difficult setting, incorporating background distractors, as a first step towards addressing this challenge. We present a simple baseline approach that can learn meaningful representations with no metric-based learning, no data augmentations, no world-model learning, and no contrastive learning. We then analyze when and why previously proposed methods are likely to fail or reduce to the same performance as the baseline in this harder setting and why we should think carefully about extending such methods beyond the well curated environments. Our results show that finer categorization of benchmarks on the basis of characteristics like density of reward, planning horizon of the problem, presence of task-irrelevant components, etc., is crucial in evaluating algorithms. Based on these observations, we propose different metrics to consider when evaluating an algorithm on benchmark tasks. We hope such a data-centric view can motivate researchers to rethink representation learning when investigating how to best apply RL to real-world tasks.