Chapter
The Role of Deep Reinforcement Learning in Near Optimal Control
53:49 - 1:05:25 (11:35)
A discussion on the combination of reinforcement learning algorithms with high capacity neural net representations and how it leads to near optimal control without the need for a complete model of the world.
Clips
When dealing with AI models, predicting scenarios that were not seen during training can be a challenge as the model may not have the capacity to do so.
53:49 - 56:41 (02:52)
Summary
When dealing with AI models, predicting scenarios that were not seen during training can be a challenge as the model may not have the capacity to do so. Developing algorithms may be able to bridge this gap and help in predicting unforeseen scenarios.
ChapterThe Role of Deep Reinforcement Learning in Near Optimal Control
Episode#108 – Sergey Levine: Robotics and Machine Learning
PodcastLex Fridman Podcast
Deep Reinforcement Learning combines reinforcement learning algorithms with high-capacity neural net representations in order to obtain a near optimal control or policy without having a complete model of the world.
56:41 - 1:01:22 (04:40)
Summary
Deep Reinforcement Learning combines reinforcement learning algorithms with high-capacity neural net representations in order to obtain a near optimal control or policy without having a complete model of the world. Through this, the field of reinforcement learning provides a mathematically principled framework to optimize an unknown system without having knowledge of the equations that govern it.
ChapterThe Role of Deep Reinforcement Learning in Near Optimal Control
Episode#108 – Sergey Levine: Robotics and Machine Learning
PodcastLex Fridman Podcast
The progress of deep reinforcement learning in tackling complex problems such as robotics and gaming is impressive, but the challenges of gathering highly varied, large and complex datasets limit its potential.
1:01:22 - 1:05:25 (04:03)
Summary
The progress of deep reinforcement learning in tackling complex problems such as robotics and gaming is impressive, but the challenges of gathering highly varied, large and complex datasets limit its potential.