论文标题
对深度强化学习的不确定性的评论
A Review of Uncertainty for Deep Reinforcement Learning
论文作者
论文摘要
不确定性在游戏中无处不在，无论是在玩游戏的代理商还是在游戏本身中。因此，与不确定性一起工作是成功加强学习剂的重要组成部分。尽管在理解和处理监督学习的不确定性方面已经做出了巨大的努力和进步，但不确定性的文献意识到深度强化学习的发展却较少。尽管有关对监督学习的神经网络中不确定性的许多相同问题仍然用于强化学习，但由于可相互作用的环境的性质，还有其他不确定性来源。在这项工作中，我们提供了一个概述，以激励和介绍不确定性意识到深度强化学习的现有技术。这些作品在各种强化学习任务上显示出经验益处。这项工作有助于集中不同的结果并促进该领域的未来研究。
Uncertainty is ubiquitous in games, both in the agents playing games and often in the games themselves. Working with uncertainty is therefore an important component of successful deep reinforcement learning agents. While there has been substantial effort and progress in understanding and working with uncertainty for supervised learning, the body of literature for uncertainty aware deep reinforcement learning is less developed. While many of the same problems regarding uncertainty in neural networks for supervised learning remain for reinforcement learning, there are additional sources of uncertainty due to the nature of an interactable environment. In this work, we provide an overview motivating and presenting existing techniques in uncertainty aware deep reinforcement learning. These works show empirical benefits on a variety of reinforcement learning tasks. This work serves to help to centralize the disparate results and promote future research in this area.
