Development of an Autonomous Agent based on Reinforcement Learning for a Digital Fighting Game

Autor:	Joao Ribeiro Bezerra, Luís F. W. Góes, Alysson Ribeiro da Silva
Rok vydání:	2020
Předmět:	Development (topology) Artificial neural network Computer science business.industry Autonomous agent Reinforcement learning Cognition Artificial intelligence Architecture business Associative property
Zdroj:	SBGames
Popis:	In this work, an autonomous agent based on reinforcement learning is implemented in a digital fighting game. The implemented agent uses Fusion Architecture for Learning, COgnition, and Navigation (FALCON) and Associative Resonance Map (ARAM) neural networks. The experimental results show that the autonomous agent is able to develop game strategies using the experience acquired in the matches, and achieves a winning rate of up to 90% against an agent with fixed behavior.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::6c23a6a97b1d6ecd373f7be4b07192c0 https://doi.org/10.1109/sbgames51465.2020.00017 Zobrazit plný text záznamu