Lecture
Policy Gradient Methods: Binary Actor Example
In course
DEMO: minim qui consequat
Reprehenderit commodo id esse sint minim occaecat. Irure fugiat anim occaecat eiusmod exercitation dolor reprehenderit aliqua amet occaecat consequat occaecat aliquip ex. Ullamco sint ad sunt dolore. Culpa excepteur veniam Lorem officia ipsum cillum duis proident consequat consectetur. Ullamco qui id aute ex fugiat duis in qui. Consectetur occaecat culpa irure incididunt.
Description
This lecture introduces policy gradient methods using a simple example of a single neuron with binary output, focusing on the disadvantages of Q-learning, SARSA, and TD-learning, and explaining the basic idea of policy gradient methods to optimize rewards directly.
Instructor
aliqua esse pariatur
Velit laborum cillum exercitation cupidatat ea. Sunt elit pariatur ut dolor labore ad reprehenderit cillum sunt nisi velit exercitation quis. Reprehenderit nisi dolor aliqua excepteur qui fugiat minim Lorem exercitation aliquip sit aliqua voluptate ut.
Official source
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Ontological neighbourhood
Related lectures (33)
Neural Networks: Deep Neural Networks
Explores the basics of neural networks, with a focus on deep neural networks and their architecture and training.
Policy Gradient Methods: Single Neuron Example
Covers policy gradient methods using a single neuron with binary output.
Deep Learning for Autonomous Vehicles: Learning
Explores learning in deep learning for autonomous vehicles, covering predictive models, RNN, ImageNet, and transfer learning.
Neural Networks Optimization
Explores neural networks optimization, including backpropagation, batch normalization, weight initialization, and hyperparameter search strategies.
Multilayer Neural Networks: Deep Learning
Covers the fundamentals of multilayer neural networks and deep learning.