

Reinforcement Learning Deep Dive with Pieter Abbeel - TWiML Talk #28
Jun 17, 2017
Pieter Abbeel, a leading researcher in deep reinforcement learning and Assistant Professor at UC Berkeley, shares his expertise in AI and robotics. He discusses the evolution of deep reinforcement learning and the integration of established physics into modern systems. Pieter elaborates on how games aid training and the intricacies of transfer learning. He also analyzes Q-learning and policy gradients, emphasizing their significance in decision-making processes. This conversation dives deep into the technical challenges and future possibilities of AI.
Chapters
Transcript
Episode notes
1 2 3 4 5 6 7
Intro
00:00 • 2min
Advancements in Deep Reinforcement Learning
01:42 • 19min
Obfuscating Neural Network Features for Enhanced Performance
20:44 • 2min
The Role of Games in Reinforcement Learning Training
22:33 • 3min
Exploring Transfer Learning and Its Challenges
25:58 • 3min
Exploring Q-learning and Policy Gradients in Reinforcement Learning
29:02 • 21min
Engagement and Upcoming Events in AI
50:16 • 2min