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Distinguish different types of reinforcement learning algorithms and when to use them. Describe the benefits and potential challenges of deep reinforcement ...
Broad topics covered in this course will include: Markov decision processes, reinforcement learning algo- rithms (model-free, batch/online, ...
4 Course Outline. A tentative schedule of topics to be covered in each lecture, and tentative deadlines for homeworks and the project (in bold), are given in ...
The course provides an introduction to the theory and applications of Reinforcement Learning (RL). RL is an effec- tive approach within Machine Learning ...
This course aims to teach the fundamentals and the advanced topics of Reinforcement Learning. (RL), a computational learning approach where an agent tries ...
Reinforcement Learning is a subarea of Machine Learning concerned with computational artifacts that modify and improve their performance through experience. One ...
The main goal of this study is to improve the training efficiency of reinforcement learning using curriculum learning. Although an extensive survey of ...
We study the problem of how to guide the training of Rein- forcement Learned skills using a variant of Hierarchical Goal. Networks. Using goals to focus ...
The material will promote the retention of the concepts of machine leaning by delivering the content through more effective methods of instruction and learning.
Reinforcement Learning (RL) is a machine learning paradigm that enables artificial agents to learn a strategy for behaving in order to maximise ...
