Diminishing Return of Value Expansion Methods

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, in the class of state-based, dense reward control tasks studied, the limitation of model-based value expansion methods cannot be attributed solely to model accuracy. Although higher accuracy is beneficial, even perfect models do not provide unrivaled sample efficiency, indicating that other factors limit performance. These results challenge the common assumption that model accuracy is the primary constraint in model-based reinforcement learning, and highlight the need for future work to identify the true limiting factors.