Data Augmentation for Image-Based Reinforcement Learning

Explore cutting-edge techniques in data augmentation for image-based reinforcement learning in this 52-minute seminar by Rob Fergus at MIT. Delve into a model-free reinforcement learning algorithm for visual continuous control that achieves state-of-the-art results on the DeepMind Control Suite, including complex humanoid locomotion. Learn about a self-supervised framework that combines representation learning with exploration through prototypical representations. Discover how pre-trained task-agnostic representations and prototypes enable superior downstream policy learning on challenging continuous control tasks. Gain insights into the latest advancements in computer vision, reinforcement learning, and artificial intelligence from a leading expert in the field.

Introduction
Outline
Problem
Image Augmentation
Other Augmentation Strategies
Hyper Parameters
Models and Auxiliary Tasks
Results
Atari Benchmark
Image Augmentations
Summary
Dr Q
Dr Qv2
Dreamer
Conclusion
Reinforcement with prototypical representations
Limitations
Task Exploration
Selfsupervised Learning
ProtoRL Approach
Example
Importance of Exploration
Benchmarking
Wrapup