In partially observable (PO) environments, deep reinforcement learning (RL)
agents often suffer from unsatisfactory performance, since two problems need to
be tackled together: how to extract information from the raw observations to
solve the task, and how to improve the policy. In this study, we propose an RL
algorithm for solving PO tasks. Our method comprises two parts: a variational
recurrent model (VRM) for modeling the environment, and an RL controller that
has access to both the environment and the VRM. The proposed algorithm was
tested in two types of PO robotic control tasks, those in which either
coordinates or velocities were not observable and those that require long-term
memorization. Our experiments show that the proposed algorithm achieved better
data efficiency and/or learned more optimal policy than other alternative
approaches in tasks in which unobserved states cannot be inferred from raw
observations in a simple manner.

本文提出了一种在部分可观察环境下应用深度强化学习解决机器人控制任务的算法，该算法包含了两个部分，即可变循环模型和强化学习控制器；实验证明，该算法比其他方法在数据效率和策略学习上表现更好。