Automatic Curriculum Learning for Deep RL: a Short Survey

This video is a 15min presentation of a survey paper on Automatic Curriculum Learning that we presented at IJCAI2020.

Abstract:

Automatic Curriculum Learning (ACL) has become a cornerstone of recent successes in Deep Reinforcement Learning (DRL).These methods shape the learning trajectories of agents by challenging them with tasks adapted to their capacities. In recent years, they have been used to improve sample efficiency and asymptotic performance, to organize exploration, to encourage generalization or to solve sparse reward problems, among others. The ambition of this work is dual: 1) to present a compact and accessible introduction to the Automatic Curriculum Learning literature and 2) to draw a bigger picture of the current state of the art in ACL to encourage the cross-breeding of existing concepts and the emergence of new ideas.

please cite as:

Rémy Portelas, Cédric Colas, Lilian Weng, Katja Hofmann, and Pierre-Yves Oudeyer. Automatic Curriculum Learning for Deep RL: A short survey. IJCAI 2020.

Authors' affiliations:

Rémy Portelas - Inria Bordeaux
Cédric Colas - Inria Bordeaux
Lilian Weng - OpenAI
Katja Hofmann - Microsoft Research Cambridge
Pierre-Yves Oudeyer - Inria Bordeaux