论文标题
本质上动机的增强学习：简要介绍
Intrinsically-Motivated Reinforcement Learning: A Brief Introduction
论文作者
论文摘要
增强学习（RL）是机器学习的三个基本范式之一。它在GO和Starcraft等许多复杂任务中表现出了令人印象深刻的表现，这越来越多地参与智能制造和自动驾驶。但是，RL始终遭受探索 - 诠释困境。在本文中，我们研究了改善RL探索的问题，并引入了内在动机的RL。与经典的探索策略形成鲜明对比的是，内在动机的RL利用内在的学习动机来提供可持续的探索激励措施。我们仔细地对现有的内在奖励方法进行了分类，并分析了它们的实际缺点。此外，我们通过RényiState Entropy最大化提出了一种新的内在奖励方法，该方法克服了前面方法的缺点，并提供了强大的探索激励措施。最后，广泛的模拟表明，提出的模块以更高的效率和鲁棒性实现了出色的性能。
Reinforcement learning (RL) is one of the three basic paradigms of machine learning. It has demonstrated impressive performance in many complex tasks like Go and StarCraft, which is increasingly involved in smart manufacturing and autonomous driving. However, RL consistently suffers from the exploration-exploitation dilemma. In this paper, we investigated the problem of improving exploration in RL and introduced the intrinsically-motivated RL. In sharp contrast to the classic exploration strategies, intrinsically-motivated RL utilizes the intrinsic learning motivation to provide sustainable exploration incentives. We carefully classified the existing intrinsic reward methods and analyzed their practical drawbacks. Moreover, we proposed a new intrinsic reward method via Rényi state entropy maximization, which overcomes the drawbacks of the preceding methods and provides powerful exploration incentives. Finally, extensive simulation demonstrated that the proposed module achieve superior performance with higher efficiency and robustness.
