Transfer Learning in Real-Time Strategy Games Using Hybrid CBR/RL

TitleTransfer Learning in Real-Time Strategy Games Using Hybrid CBR/RL
Publication TypeClassical
Year of Publication2007
AuthorsSharma M, Holmes M, Santamaria J, Irani A, Isbell C, Ram A
CityGeorgia
Abstract

The goal of transfer learning is to use the knowledge
acquired in a set of source tasks to improve
performance in a related but previously unseen
target task. In this paper, we present a multilayered
architecture named CAse-Based Reinforcement
Learner (CARL). It uses a novel combination
of Case-Based Reasoning (CBR) and Reinforcement
Learning (RL) to achieve transfer while
playing against the Game AI across a variety of
scenarios in MadRTSTM, a commercial Real Time
Strategy game. Our experiments demonstrate that
CARL not only performs well on individual tasks
but also exhibits significant performance gains
when allowed to transfer knowledge from previous
tasks.

URLhttp://www.aaai.org/Papers/IJCAI/2007/IJCAI07-168.pdf