Introduction to Behavior Algorithms for Fighting Games

2020-07-06 · Ignacio Gajardo, Felipe Besoain, Nicolas A. Barriga

The quality of opponent Artificial Intelligence (AI) in fighting videogames is crucial. Some other game genres can rely on their story or visuals, but fighting games are all about the adversarial experience. In this paper, we will introduce standard behavior algorithms in videogames, such as Finite-State Machines and Behavior Trees, as well as more recent developments, such as Monte-Carlo Tree Search. We will also discuss the existing and potential combinations of these algorithms, and how they might be used in fighting games. Since we are at the financial peak of fighting games, both for casual players and in tournaments, it is important to build and expand on fighting game AI, as it is one of the pillars of this growing market.

📄 PDF Abstract BibTeX arXiv:2007.12586

Code (0)

등록된 구현이 없습니다.

Methods 이 논문이 사용한 방법론

Monte-Carlo Tree Search Monte-Carlo Tree Search is a planning algorithm that accumulates value estimates obtained from Monte Carlo simulations in order to successively direct simulations towards more…

Deep Reinforcement Learning for Playing 2.5D Fighting Games

2018-05-05 · Yu-Jhe Li, Hsin-Yu Chang, Yu-Jing Lin, Po-Wei Wu 외

Deep reinforcement learning has shown its success in game playing. However, 2.5D fighting games would be a challenging task to handle due to ambiguity in visual appearances like height or depth of the characters. Moreove…

Deep Reinforcement Learning OpenAI Gym reinforcement-learning Reinforcement Learning+1

Advancing DRL Agents in Commercial Fighting Games: Training, Integration, and Agent-Human Alignment

2024-06-03 · Chen Zhang, Qiang He, Zhou Yuan, Elvis S. Liu 외

Deep Reinforcement Learning (DRL) agents have demonstrated impressive success in a wide range of game genres. However, existing research primarily focuses on optimizing DRL competence rather than addressing the challenge…

Deep Reinforcement Learning

FootsiesGym: A Fighting Game Benchmark for Two-Player Zero-Sum Imperfect-Information Games

2026-07-07 · Chase McDonald, Nathan Tsang, Wesley N. Kerr arxiv

We present FootsiesGym, an open-source environment for learning in a non-trivial two-player, zero-sum, imperfect-information game. Built on HiFight's minimalist 2D fighting game Footsies, it isolates the cyclic, non-tran…

Reinforcement Learning

DareFightingICE Competition: A Fighting Game Sound Design and AI Competition

2022-03-03 · Ibrahim Khan, Thai Van Nguyen, Xincheng Dai, Ruck Thawonmas

This paper presents a new competition -- at the 2022 IEEE Conference on Games (CoG) -- called DareFightingICE Competition. The competition has two tracks: a sound design track and an AI track. The game platform for this …

Diversity-based Deep Reinforcement Learning Towards Multidimensional Difficulty for Fighting Game AI

2022-11-04 · Emily Halina, Matthew Guzdial

In fighting games, individual players of the same skill level often exhibit distinct strategies from one another through their gameplay. Despite this, the majority of AI agents for fighting games have only a single strat…

Deep Reinforcement Learning Diversity reinforcement-learning Reinforcement Learning (RL)