Reinforcement Learning

Study machine learning at a deeper level and become a participant in the reinforcement learning research community.

Reinforcement Learning

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SchoolUdacity
ScheduleOn Demand
LocationOnline
Duration4 months
Credits0
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Reinforcement Learning

* Reinforcement Learning Basics * Introduction to BURLAP * TD Lambda * Convergence of Value and Policy Iteration * Reward Shaping * Exploration * Generalization * Partially Observable MDPs * Options * Topics in Game Theory * Further Topics in RL Models

You should take this course if you have an interest in machine learning and the desire to engage with it from a theoretical perspective. Through a combination of classic papers and more recent work, you will explore automated decision-making from a computer-science perspective. You will examine efficient algorithms, where they exist, for single-agent and multi-agent planning as well as approaches to learning near-optimal decisions from experience. At the end of the course, you will replicate a result from a published paper in reinforcement learning.


Course provided by: Udacity