Logical Composition for Lifelong Reinforcement Learning

Abstract

The ability to produce novel behaviours from existing skills is an important property of lifelong-learning agents. We build on recent work which formalises a Boolean algebra over the space of tasks and value functions, and show how this can be leveraged to tackle the lifelong learning problem. We propose an algorithm that determines whether a new task can be immediately solved using an agent’s existing abilities, or whether the task should be learned from scratch. We verify our approach in the Four Rooms domain, where an agent learns a set of skills throughout its lifetime, and then composes them to solve a combinatorially large number of new tasks in a zero-shot manner.

Geraud Nangue Tasse

Associate Lecturer

I am an IBM PhD fellow interested in reinforcement learning (RL) since it is the subfield of machine learning with the most potential for achieving AGI.

Steven James

Deputy Lab Director

My research interests include reinforcement learning and planning.

Benjamin Rosman

Lab Director

I am a Professor in the School of Computer Science and Applied Mathematics at the University of the Witwatersrand in Johannesburg. I work in robotics, artificial intelligence, decision theory and machine learning.