YoVDO

Can Wikipedia Help Offline Reinforcement Learning? - Paper Explained

Offered By: Yannic Kilcher via YouTube

Tags

Reinforcement Learning Courses Language Models Courses Sequence Modeling Courses Offline Reinforcement Learning Courses

Course Description

Overview

Explore a comprehensive analysis of a research paper examining the potential of Wikipedia to enhance offline reinforcement learning. Delve into the innovative approach of treating reinforcement learning as sequence modeling, leveraging pre-trained language models to improve performance in control and game tasks. Discover how this method accelerates training by 3-6 times and achieves state-of-the-art results across various environments. Gain insights into the experimental findings, attention pattern analysis, and scaling properties of this novel technique. Understand the implications for bridging the gap between language modeling and reinforcement learning, opening new avenues for knowledge transfer between seemingly disparate domains.

Syllabus

- Intro
- Paper Overview
- Offline Reinforcement Learning as Sequence Modelling
- Input Embedding Alignment & other additions
- Main experimental results
- Analysis of the attention patterns across models
- More experimental results scaling properties, ablations, etc.
- Final thoughts


Taught by

Yannic Kilcher

Related Courses

Microsoft Bot Framework and Conversation as a Platform
Microsoft via edX
Unlocking the Power of OpenAI for Startups - Microsoft for Startups
Microsoft via YouTube
Improving Customer Experiences with Speech to Text and Text to Speech
Microsoft via YouTube
Stanford Seminar - Deep Learning in Speech Recognition
Stanford University via YouTube
Select Topics in Python: Natural Language Processing
Codio via Coursera