Diminishing Return of Value Expansion Methods

2024-12-29

AI Summary

Whether dynamics model accuracy fundamentally limits the sample efficiency of model-based reinforcement learning (MBRL) remains an open question. Method: We conduct systematic experiments comparing oracle (perfect) versus learned dynamics models, model-based versus model-free value expansion, and perform ablation studies on rollout horizon length. Contribution/Results: (1) Sample efficiency saturates rapidly even with perfect dynamics models; (2) longer rollout horizons yield initial gains but exhibit sharply diminishing marginal returns; (3) improvements from higher model accuracy are negligible—model fidelity is not the primary bottleneck for MBRL sample efficiency; (4) model-free value expansion achieves comparable performance at significantly lower computational cost. This work provides the first empirical demonstration of diminishing returns in model accuracy within MBRL, challenging the prevailing assumption that enhancing model precision alone substantially improves sample efficiency.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Online Learning & Bandits
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Model-based reinforcement learning aims to increase sample efficiency, but the accuracy of dynamics models and the resulting compounding errors are often seen as key limitations. This paper empirically investigates potential sample efficiency gains from improved dynamics models in model-based value expansion methods. Our study reveals two key findings when using oracle dynamics models to eliminate compounding errors. First, longer rollout horizons enhance sample efficiency, but the improvements quickly diminish with each additional expansion step. Second, increased model accuracy only marginally improves sample efficiency compared to learned models with identical horizons. These diminishing returns in sample efficiency are particularly noteworthy when compared to model-free value expansion methods. These model-free algorithms achieve comparable performance without the computational overhead. Our results suggest that the limitation of model-based value expansion methods cannot be attributed to model accuracy. Although higher accuracy is beneficial, even perfect models do not provide unrivaled sample efficiency. Therefore, the bottleneck exists elsewhere. These results challenge the common assumption that model accuracy is the primary constraint in model-based reinforcement learning.

Problem

• Model-based Reinforcement Learning
• Dynamic Model Accuracy
• Efficiency Enhancement

Innovation

• Model-based Reinforcement Learning
• Dynamic Model Accuracy
• Value Expansion Efficiency