论文标题
使用基于期望最大化的指导性策略搜索随机动态的增强学习
Reinforcement Learning Using Expectation Maximization Based Guided Policy Search for Stochastic Dynamics
论文作者
论文摘要
已证明，有指导的政策搜索算法不仅可以控制复杂的动态系统，而且还可以从各种看不见的实例中学习最佳策略。在几乎所有众所周知的政策搜索和学习算法中，都假定国家的真实本质。本文介绍了未知动态系统的轨迹优化过程，但使用预期最大化，将其扩展到学习（最佳）策略，该策略的噪声较小，因为最佳轨迹的差异较小。与一些知名的基线相比，描述了新方法最佳政策的理论和经验证据，这些基线在具有广泛使用的性能指标的自主系统上进行了评估。
Guided policy search algorithms have been proven to work with incredible accuracy for not only controlling a complicated dynamical system, but also learning optimal policies from various unseen instances. One assumes true nature of the states in almost all of the well known policy search and learning algorithms. This paper deals with a trajectory optimization procedure for an unknown dynamical system subject to measurement noise using expectation maximization and extends it to learning (optimal) policies which have less noise because of lower variance in the optimal trajectories. Theoretical and empirical evidence of learnt optimal policies of the new approach is depicted in comparison to some well known baselines which are evaluated on an autonomous system with widely used performance metrics.
