---
title: "Learning to Achieve Goals"
authors:
  - "Leslie Pack Kaelbling"
doi: ""
url: "https://ijcai.org/proceedings/1993/151"
pdf: "https://ijcai.org/proceedings/1993/0151.pdf"
venue: "Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence"
year: 1993
pages: "1094-1098"
track: "Machine Learning"
keywords:
---

# Learning to Achieve Goals

Leslie Pack Kaelbling

*Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence*, pages 1094-1098.

## Abstract

Temporal difference methods solve the temporal credit assignment problem for reinforcement learning. An important subproblem of general reinforcement learning is learning to achieve dynamic goals. Although existing temporal difference methods, such as Q learning, can be applied to this problem , they do not take advantage of its special structure. This paper presents the DG-learning algorithm, which learns efficiently to achie ve dynamically changing goals and exhibits good knowledge transfer between goals. In addition, this paper shows how traditional relaxation techniques can be applied to the problem . Finally, experimental results ar e given that demonstrate the superiority of DG learning over Q learning in a moderately large, synthetic , non-deterministic domain .