---
title: "Transfer Learning in Real-Time Strategy Games Using Hybrid CBR/RL"
authors:
  - "Manu Sharma"
  - "Michael P. Holmes"
  - "Juan Carlos Santamaría"
  - "Arya Irani"
  - "Charles Lee Isbell Jr."
  - "Ashwin Ram"
doi: ""
url: "https://ijcai.org/proceedings/2007/168"
pdf: "https://ijcai.org/proceedings/2007/0168.pdf"
venue: "Proceedings of the Twentieth International Joint Conference on Artificial Intelligence"
year: 2007
pages: "1041-1045"
track: ""
keywords:
---

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

Manu Sharma, Michael P. Holmes, Juan Carlos Santamaría, Arya Irani, Charles Lee Isbell Jr., Ashwin Ram

*Proceedings of the Twentieth International Joint Conference on Artificial Intelligence*, pages 1041-1045.

## 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 MadRTS(TM), 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.