Diverse Behavior Is What Game AI Needs: Generating Varied Human-Like Playing Styles Using Evolutionary Multi-Objective Deep Reinforcement Learning

2019/10/20 by Ruimin Shen, Shen, Ruimin, Yan Zheng +7 · 1 citation
Computer Science · Mathematics · #Artificial Intelligence in Games #Evolutionary Algorithms and Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Reinforcement Learning in Robotics #cs.LG #stat.ML

paper · pdf · doi:10.48550/arxiv.1910.09022

1. there is some discrepancy between some contributors with respect to the order of the authors; 2. the paper is rather "raw" - significant effort and improvement in terms of the paper's language and structure are needed to make it ready for publication

openalex publication_date 2019/10/20 · openalex created_date 2019/10/25 · arxiv created 2020/04/16 · arxiv updated 2020/04/17 · openalex updated_date 2026/07/28

Abstract

this paper has been withdrawn

Citations

• Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning
• A Generalized Framework for Population Based Training
• Counterfactual Multi-Agent Policy Gradients

Cited by

• Offline Learning of Controllable Diverse Behaviors

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