Reinforcement learning (RL) has been shown to learn sophisticated control policies for complex tasks including games, robotics, heating and cooling systems and text generation. The action-perception cycle in RL, however, generally assumes that a measurement of the state of the environment is available at each time step without a cost. In applications such as deep-sea and planetary robot exploration, materials design and medicine, however, there can be a high cost associated with measuring, or even approximating, the state of the environment. In this paper, we survey the recently growing literature that adopts the perspective that an RL agent might not need, or even want, a costly measurement at each time step. Within this context, we propose the Deep Dynamic Multi-Step Observationless Agent (DMSOA), contrast it with the literature and empirically evaluate it on OpenAI gym and Atari Pong environments. Our results, show that DMSOA learns a better policy with fewer decision steps and measurements than the considered alternative from the literature.

本文调查了最近发展的文献，采用了RL代理不需要每个时间步都进行昂贵测量的观点，并在OpenAI gym和Atari Pong环境上与文献中的替代方法进行对比和实证评估，结果表明DMSOA在较少的决策步骤和测量下学习到了更好的策略。

观测代价敏感强化学习中的动态观测策略