---
title: "State Similarity Based Approach for Improving Performance in RL"
authors:
  - "Sertan Girgin"
  - "Faruk Polat"
  - "Reda Alhajj"
doi: ""
url: "https://ijcai.org/proceedings/2007/131"
pdf: "https://ijcai.org/proceedings/2007/0131.pdf"
venue: "Proceedings of the Twentieth International Joint Conference on Artificial Intelligence"
year: 2007
pages: "817-821"
track: ""
keywords:
---

# State Similarity Based Approach for Improving Performance in RL

Sertan Girgin, Faruk Polat, Reda Alhajj

*Proceedings of the Twentieth International Joint Conference on Artificial Intelligence*, pages 817-821.

## Abstract

This paper employs state similarity to improve reinforcement learning performance. This is achieved by first identifying states with similar sub-policies. Then, a tree is constructed to be used for locating common action sequences of states as derived from possible optimal policies. Such sequences are utilized for defining a similarity function between states, which is essential for reflecting updates on the action-value function of a state onto all similar states. As a result, the experience acquired during learning can be applied to a broader context. Effectiveness of the method is demonstrated empirically.