Towards Adaptive Deep Reinforcement Game Balancing

Ashey Noblega, Aline Paes, Esteban Clua

Abstract

The experience of a player regarding the difficulty of a video game is one of the main reasons for he/she decide to keep playing the game or abandon it. Effectively, player retention is one of the primary concerns related to the game development process. However, the experience of a player with a game is unique, making impractical to anticipate how they will face the gameplay. This work leverages the recent advances in Reinforcement Learning (RL) and Deep Learning (DL) to create intelligent agents that are able to adapt to the abilities of distinct players. We focus on balancing the difficulty of the game based on the information that the agent observes from the 3D environment as well as the current state of the game. In order to design an agent that learns how to act while still maintaining the balancing, we propose a reward function based on a balancing constant. We require that the agent remains inside a range around this constant during the training. Our experimental results show that by using such a reward function and combining information from different types of players it is possible to have adaptable agents that fit the player.

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Paper Citation


in Harvard Style

Noblega A., Paes A. and Clua E. (2019). Towards Adaptive Deep Reinforcement Game Balancing.In Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-350-6, pages 693-700. DOI: 10.5220/0007395406930700


in Bibtex Style

@conference{icaart19,
author={Ashey Noblega and Aline Paes and Esteban Clua},
title={Towards Adaptive Deep Reinforcement Game Balancing},
booktitle={Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2019},
pages={693-700},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007395406930700},
isbn={978-989-758-350-6},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Towards Adaptive Deep Reinforcement Game Balancing
SN - 978-989-758-350-6
AU - Noblega A.
AU - Paes A.
AU - Clua E.
PY - 2019
SP - 693
EP - 700
DO - 10.5220/0007395406930700