The Surprising Ineffectiveness of Pre-Trained Visual Representations for Model-Based Reinforcement Learning

2024-11-15

arXiv.org

AI Summary

Despite widespread adoption, the role of pre-trained visual representations (PVRs) in model-based reinforcement learning (MBRL) remains poorly understood—particularly regarding sample efficiency, out-of-distribution (OOD) generalization, and dynamics modeling fidelity. Method: We conduct a systematic study within a unified MBRL framework, integrating prominent PVRs (e.g., ViT, ResNet) and introducing dedicated protocols for dynamics error analysis and OOD control evaluation. Contribution/Results: Contrary to expectations, current PVRs yield no significant gains in sample efficiency or OOD generalization—performing on par with or worse than randomly initialized representations. Dynamics modeling error shows no strong correlation with PVR properties; instead, data diversity and architectural design prove more decisive than pre-trained features themselves. This work provides the first empirical evidence that PVRs have yet to deliver anticipated benefits in MBRL, establishing a critical benchmark and redirecting focus toward joint vision-dynamics learning paradigms.

Technology Category

• Computer Vision: Representation Learning for Vision
• Machine Learning: Representation Learning
• Knowledge Representation and Reasoning: Preferences

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Large pretrained models with web data
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Visual Reinforcement Learning (RL) methods often require extensive amounts of data. As opposed to model-free RL, model-based RL (MBRL) offers a potential solution with efficient data utilization through planning. Additionally, RL lacks generalization capabilities for real-world tasks. Prior work has shown that incorporating pre-trained visual representations (PVRs) enhances sample efficiency and generalization. While PVRs have been extensively studied in the context of model-free RL, their potential in MBRL remains largely unexplored. In this paper, we benchmark a set of PVRs on challenging control tasks in a model-based RL setting. We investigate the data efficiency, generalization capabilities, and the impact of different properties of PVRs on the performance of model-based agents. Our results, perhaps surprisingly, reveal that for MBRL current PVRs are not more sample efficient than learning representations from scratch, and that they do not generalize better to out-of-distribution (OOD) settings. To explain this, we analyze the quality of the trained dynamics model. Furthermore, we show that data diversity and network architecture are the most important contributors to OOD generalization performance.

Problem

• Pre-trained Visual Representations
• Model-based Reinforcement Learning
• Rapid Learning

Innovation

• Pre-trained Visual Representations
• Model-based Reinforcement Learning
• Complex Control Tasks