Introduction to Reinforcement Learning
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Reinforcement learning is a branch of machine learning, in which in algorithm learns a good policy for acting in an environment of interest, based on experience. When interacting with the environment, an agent (algorithm) experiences rewards or costs, based upon actions taken in particular situations. The algorithm learns a policy by “reinforcing” (learning to favor) actions that yield high rewards. In this introduction to reinforcement learning, basic concepts like Q-learning will be developed. In addition, we will discuss how deep learning (neural networks) have impacted reinforcement learning, including a discussion of how the Alpha-Go system developed by Google Deep Mind was able to defeat the best human players in the game Go.
|Date||Thursday, February 21st, 2019|
|Time||4:00pm - 6:00pm|
|Location||The Edge Workshop|
|Enrolled||50 of 50|