Monte Carlo Tree Search has proven to be one of the most productive techniques in Reinforcement Learning. It has produced state of the results in problems with humongous state spaces like Chess and Go. Ali will present the theory behind this Monte Carlo Tress Search and share its use cases.
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Monte Carlo Tree Search in Reinforcement Learning
Ali is a self-taught programmer, currently doing a PhD in Artificial Intelligence and Education at University College London. His research focuses on applying human-centered learning science theories on Deep Reinforcement Learning algorithms. These days he's obsessed with GANs and Variational Autoencoders.