论文标题
关于加强学习内在动机的信息理论观点：一项调查
An information-theoretic perspective on intrinsic motivation in reinforcement learning: a survey
论文作者
论文摘要
增强学习（RL）研究领域非常活跃，并具有重要的新贡献；特别是考虑到深RL（DRL）的新兴领域。但是，仍然需要解决许多科学和技术挑战，其中我们可以提到抽象行动的能力或在稀疏回报环境中探索环境的难以通过内在动机（IM）来解决的。我们建议通过基于信息理论的新分类法调查这些研究工作：我们在计算上重新审视了惊喜，新颖性和技能学习的概念。这使我们能够确定方法的优势和缺点，并展示当前的研究前景。我们的分析表明，新颖性和惊喜可以帮助建立可转移技能的层次结构，从而进一步提出环境，并使探索过程更加健壮。
The reinforcement learning (RL) research area is very active, with an important number of new contributions; especially considering the emergent field of deep RL (DRL). However a number of scientific and technical challenges still need to be resolved, amongst which we can mention the ability to abstract actions or the difficulty to explore the environment in sparse-reward settings which can be addressed by intrinsic motivation (IM). We propose to survey these research works through a new taxonomy based on information theory: we computationally revisit the notions of surprise, novelty and skill learning. This allows us to identify advantages and disadvantages of methods and exhibit current outlooks of research. Our analysis suggests that novelty and surprise can assist the building of a hierarchy of transferable skills that further abstracts the environment and makes the exploration process more robust.
