Probabilistic world models increase data efficiency of model-based
reinforcement learning (MBRL) by guiding the policy with their epistemic
uncertainty to improve exploration and acquire new samples. Moreover, the
uncertainty-aware learning procedures in probabilistic approaches lead to
robust policies that are less sensitive to noisy observations compared to
uncertainty unaware solutions. We propose to combine trajectory sampling and
deep Gaussian covariance network (DGCN) for a data-efficient solution to MBRL
problems in an optimal control setting. We compare trajectory sampling with
density-based approximation for uncertainty propagation using three different
probabilistic world models; Gaussian processes, Bayesian neural networks, and
DGCNs. We provide empirical evidence using four different well-known test
environments, that our method improves the sample-efficiency over other
combinations of uncertainty propagation methods and probabilistic models.
During our tests, we place particular emphasis on the robustness of the learned
policies with respect to noisy initial states.

通过结合轨迹采样和深度高斯协方差网络（DGCN），我们提出了一种在最优控制环境中解决 MBRL 问题的数据高效解决方案，通过三种不同的概率世界模型（高斯过程、贝叶斯神经网络和 DGCNs）比较采用基于密度的不确定性传播的轨迹采样方法，我们在四个不同的知名测试环境中提供了实证证据，表明我们的方法提高了样本效率，同时对于噪声初始状态的鲁棒性也得到了重视。