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SkillsCast

Reinforcement Learning Basics: Modelling your Algorithms

14th March 2019 in London at CodeNode

This SkillsCast was filmed at Reinforcement Learning Basics: Modelling your Algorithms

In this talk, Ali Chaudhry will share the choices that one has to make in modelling a Reinforcement Learning algorithm.

Muhammad Ali Chaudhry is a PostGraduate Researcher at University College London (UCL). In this talk he will share the choices that one has to make in modelling a Reinforcement Learning algorithm. He will discuss different types of Reinforcement Learning agents like Model-free, Model-based, Value-based, Policy-based and Actor-Critic. He will also discuss the trade-off between Exploration vs Exploitation and Planning vs Control using Temporal Difference Learning, Dynamic Programming and Monte Carlo approaches.

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Reinforcement Learning Basics: Modelling your Algorithms

Ali Chaudhry

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.

SkillsCast

In this talk, Ali Chaudhry will share the choices that one has to make in modelling a Reinforcement Learning algorithm.

Muhammad Ali Chaudhry is a PostGraduate Researcher at University College London (UCL). In this talk he will share the choices that one has to make in modelling a Reinforcement Learning algorithm. He will discuss different types of Reinforcement Learning agents like Model-free, Model-based, Value-based, Policy-based and Actor-Critic. He will also discuss the trade-off between Exploration vs Exploitation and Planning vs Control using Temporal Difference Learning, Dynamic Programming and Monte Carlo approaches.

YOU MAY ALSO LIKE:

Thanks to our sponsors

About the Speaker

Reinforcement Learning Basics: Modelling your Algorithms

Ali Chaudhry

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.