Explore a comprehensive review of the paper "ACCEL: Evolving Curricula with Regret-Based Environment Design" in this 44-minute video. Dive into the world of automatic curriculum generation for reinforcement learning, focusing on ACCEL's innovative approach to creating multi-capable agents. Learn about the algorithm's combination of adversarial adaptiveness from regret-based sampling methods and level-editing capabilities from evolutionary methods. Follow along as the video breaks down the paper's key components, including the ACCEL algorithm, pseudocode analysis, regret approximation techniques, and experimental results. Gain insights into the potential impact of this research on developing more robust and adaptable AI agents through curriculum-based training.
ACCEL - Evolving Curricula with Regret-Based Environment Design
Overview
Syllabus
- Intro & Demonstration
- Paper overview
- The ACCEL algorithm
- Looking at the pseudocode
- Approximating regret
- Experimental results
- Discussion & Comments
Taught by
Yannic Kilcher
Related Courses
-
Author Interview - ACCEL- Evolving Curricula with Regret-Based Environment Design
-
Optimal Gradient-Based Algorithms for Non-Concave Bandit Optimization
-
Online Learning and Bandits - Part 2
-
Model-Based Reinforcement Learning with Value-Targeted Regression
-
Improving Intrinsic Exploration with Language Abstractions - Machine Learning Paper Explained
-
AMP- Adversarial Motion Priors for Stylized Physics-Based Character Control
