Designing and analyzing model-based RL (MBRL) algorithms with guaranteed monotonic improvement has been challenging, mainly due to the interdependence between policy optimization and model learning. Existing discrepancy bounds generally ignore the impacts of model shifts, and their corresponding algorithms are prone to degrade performance by drastic model updating. In this work, we first propose a novel and general theoretical scheme for a non-decreasing performance guarantee of MBRL. Our follow-up derived bounds reveal the relationship between model shifts and performance improvement. These discoveries encourage us to formulate a constrained lower-bound optimization problem to permit the monotonicity of MBRL. A further example demonstrates that learning models from a dynamically-varying number of explorations benefit the eventual returns. Motivated by these analyses, we design a simple but effective algorithm CMLO (Constrained Model-shift Lower-bound Optimization), by introducing an event-triggered mechanism that flexibly determines when to update the model. Experiments show that CMLO surpasses other state-of-the-art methods and produces a boost when various policy optimization methods are employed.

翻译：设计和分析具有保证单调改进的基于模型的强化学习（MBRL）算法一直颇具挑战，主要源于策略优化与模型学习之间的相互依赖。现有的差异界通常忽略模型偏移的影响，且其相应算法因激进的模型更新而易于导致性能下降。在本工作中，我们首先提出一个新颖且通用的理论方案，用于实现MBRL的非递减性能保证。后续推导的界揭示了模型偏移与性能改进之间的关系。这些发现促使我们构建一个约束下界优化问题，以实现MBRL的单调性。进一步实例表明，从动态变化数量的探索中学习模型有助于最终收益。受这些分析启发，我们设计了一个简单而有效的算法CMLO（约束模型偏移下界优化），通过引入事件触发机制灵活决定何时更新模型。实验表明，CMLO超越了其他最先进方法，并在采用多种策略优化方法时产生了性能提升。