The purpose of AI Magazine is to disseminate timely and informative articles that represent the current state of the art in AI and to keep its readers posted on AAAI-related matters. The articles are selected for appeal to readers engaged in research and
A paper by DeepMind scientist triggered much debate about the path to artificial intelligence. Here, we'll try to draw the line between theory and practice.
In this article, we will try to understand where On-Policy learning, Off-policy learning and offline learning algorithms fundamentally differ. Though there is a fair amount of intimidating jargon in…
Learn AI from Stanford professors Christopher Manning, Andrew Ng, and Emma Brunskill. Free online course videos in Deep Learning, Reinforcement Learning, and Natural Language Processing.
Hi Geeks, welcome to Part-3 of our Reinforcement Learning Series. In the last two blogs, we covered some basic concepts in RL and also studied the multi-armed bandit problem and its solution methods…
This program aims to reunite researchers across disciplines that have played a role in developing the theory of reinforcement learning. It will review past developments and identify promising directions of research, with an emphasis on addressing existing open problems, ranging from the design of efficient, scalable algorithms for exploration to how to control learning and planning. It also aims to deepen the understanding of model-free vs. model-based learning and control, and the design of efficient methods to exploit structure and adapt to easier environments.
A. Slivkins. (2019)cite arxiv:1904.07272Comment: The manuscript is complete, but comments are very welcome! To be published with Foundations and Trends in Machine Learning.