Model-based reinforcement learning is one approach to increase sample efficiency. However, the accuracy of the dynamics model and the resulting compounding error over modelled trajectories are commonly regarded as key limitations. A natural question to ask is: How much more sample efficiency can be gained by improving the learned dynamics models? Our paper empirically answers this question for the class of model-based value expansion methods in continuous control problems. Value expansion methods should benefit from increased model accuracy by enabling longer rollout horizons and better value function approximations. Our empirical study, which leverages oracle dynamics models to avoid compounding model errors, shows that (1) longer horizons increase sample efficiency, but the gain in improvement decreases with each additional expansion step, and (2) the increased model accuracy only marginally increases the sample efficiency compared to learned models with identical horizons. Therefore, longer horizons and increased model accuracy yield diminishing returns in terms of sample efficiency. These improvements in sample efficiency are particularly disappointing when compared to model-free value expansion methods. Even though they introduce no computational overhead, we find their performance to be on-par with model-based value expansion methods. Therefore, we conclude that the limitation of model-based value expansion methods is not the model accuracy of the learned models. While higher model accuracy is beneficial, our experiments show that even a perfect model will not provide an un-rivalled sample efficiency but that the bottleneck lies elsewhere.

翻译：基于模型的强化学习是提升样本效率的一种途径。然而，动力学模型的准确性及其在建模轨迹上产生的复合误差通常被认为是关键限制因素。一个自然的问题是：通过改进学习的动力学模型，还能获得多少样本效率的提升？本文通过实验回答了连续控制问题中基于模型的价值扩展方法类别下的这一问题。价值扩展方法应能通过更长的展开轨迹和更好的价值函数近似来受益于增强的模型准确性。我们的实证研究利用理想动力学模型来避免模型复合误差，结果表明：(1) 更长的展开轨迹提升了样本效率，但每次额外扩展步骤带来的改进收益递减；(2) 与具有相同展开长度的学习模型相比，提高模型准确性仅能带来边际性的样本效率提升。因此，更长的展开轨迹和更高的模型准确性在样本效率方面呈现收益递减规律。当与无模型的价值扩展方法相比时，这些样本效率的提升尤其令人失望。尽管后者没有引入计算开销，我们发现其性能与基于模型的价值扩展方法相当。因此，我们得出结论：基于模型的价值扩展方法的局限性不在于学习模型的准确性。虽然更高的模型准确性是有益的，但实验表明，即使采用完美模型也无法带来无与伦比的样本效率，真正的瓶颈在于其他方面。