A Distributional Perspective on Reinforcement Learning
传统的强化学习算法例如Q-learning算法学习的是state-action值函数，而这篇文章的核心是学习state-action的概率分布。 具体各简单的例子：例如我们在上班是需要经过6站地铁，每站地铁平均需要5分钟，则上上班需要30分钟。如果每个星期（5天），地铁都会出毛病，则就需要耽误耽搁一个小时。正常情况下上班的时间期望，也就是均值是30分钟，在火车出现故障的情况下，则每天的上班时间期望
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相关文章
- 1. Machine Learning A Probabilistic Perspective 1.Introduction
- 2. A thorough understanding of on-policy and off-policy in Reinforcement learning
- 3. Differentially Private Federated Learning: A Client Level Perspective
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- 6. A Survey on Transfer Learning
- 7. Introduction to Reinforcement Learning
- 8. A Unified Game-Theoretic Approach to Multi-agent Reinforcement Learning
- 9. Playing Atari with Deep Reinforcement Learning
- 10. Reinforcement Learning Exercise 4.1