标题： 随机环境中带有延迟的控制：基于模型的强化学习方法 显示英文标题

标题： Control in Stochastic Environment with Delays: A Model-based Reinforcement Learning Approach

摘要： 本文介绍了一种针对延迟反馈环境中的控制问题的新强化学习方法。具体而言，我们的方法采用随机规划，而之前的许多方法则使用确定性规划。 这种方法使我们能够在策略优化问题中嵌入风险偏好。 我们证明了这种公式可以恢复具有确定性转换的问题的最优策略。 我们将我们的策略与文献中的两种先前方法进行了对比。 我们将该方法应用于简单任务以了解其特性。 然后，我们比较了这些方法在控制多个Atari游戏时的表现。 显示英文摘要

摘要： In this paper we are introducing a new reinforcement learning method for control problems in environments with delayed feedback. Specifically, our method employs stochastic planning, versus previous methods that used deterministic planning. This allows us to embed risk preference in the policy optimization problem. We show that this formulation can recover the optimal policy for problems with deterministic transitions. We contrast our policy with two prior methods from literature. We apply the methodology to simple tasks to understand its features. Then, we compare the performance of the methods in controlling multiple Atari games.