---
title: "Heuristic Selection of Actions in Multiagent Reinforcement Learning"
authors:
  - "Reinaldo A. C. Bianchi"
  - "Carlos H. C. Ribeiro"
  - "Anna Helena Reali Costa"
doi: ""
url: "https://ijcai.org/proceedings/2007/110"
pdf: "https://ijcai.org/proceedings/2007/0110.pdf"
venue: "Proceedings of the Twentieth International Joint Conference on Artificial Intelligence"
year: 2007
pages: "690-694"
track: ""
keywords:
---

# Heuristic Selection of Actions in Multiagent Reinforcement Learning

Reinaldo A. C. Bianchi, Carlos H. C. Ribeiro, Anna Helena Reali Costa

*Proceedings of the Twentieth International Joint Conference on Artificial Intelligence*, pages 690-694.

## Abstract

This work presents a new algorithm, called Heuristically Accelerated Minimax-Q (HAMMQ), that allows the use of heuristics to speed up the well-known Multiagent Reinforcement Learning algorithm Minimax-Q. A heuristic function that influences the choice of the actions characterises the HAMMQ algorithm. This function is associated with a preference policy that indicates that a certain action must be taken instead of another. A set of empirical evaluations were conducted for the proposed algorithm in a simplified simulator for the robot soccer domain, and experimental results show that even very simple heuristics enhances significantly the performance of the multiagent reinforcement learning algorithm.