# Lifelong Learning using Eigentasks: Task Separation, Skill Acquisition, and Selective Transfer

arXiv: https://arxiv.org/abs/2007.06918 · HTML: https://mlsanity.com/paper/2007.06918/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2020.

## Built on by (most cited first, 4 of 4)

- [LOTUS: Continual Imitation Learning for Robot Manipulation Through Unsupervised Skill Discovery](https://mlsanity.com/paper/2311.02058/) (2023)
- [A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems](https://mlsanity.com/paper/2301.07799/) (2023)
- [Model-Free Generative Replay for Lifelong Reinforcement Learning: Application to Starcraft-2](https://mlsanity.com/paper/2208.05056/) (2022)
- [System Design for an Integrated Lifelong Reinforcement Learning Agent for Real-Time Strategy Games](https://mlsanity.com/paper/2212.04603/) (2022)

## Abstract (arXiv)

We introduce the eigentask framework for lifelong learning. An eigentask is a pairing of a skill that solves a set of related tasks, paired with a generative model that can sample from the skill's input space. The framework extends generative replay approaches, which have mainly been used to avoid catastrophic forgetting, to also address other lifelong learning goals such as forward knowledge transfer. We propose a wake-sleep cycle of alternating task learning and knowledge consolidation for learning in our framework, and instantiate it for lifelong supervised learning and lifelong RL. We achieve improved performance over the state-of-the-art in supervised continual learning, and show evidence of forward knowledge transfer in a lifelong RL application in the game Starcraft2.