Experience-Driven PCG via Reinforcement Learning: A Super Mario Bros Study-Reference-Cited by-同舟云学术

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Experience-Driven PCG via Reinforcement Learning: A Super Mario Bros Study

Published:2021-08-17 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE Conference on Games (CoG)
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Author:

Shu Tianye,Liu Jialin,Yannakakis Georgios N.

Funder

National Natural Science Foundation of China

Guangdong Provincial Key Laboratory

Shenzhen Fundamental Research Program

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9618888/9618891/09619124.pdf?arnumber=9619124

Cited by 20 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A mixed-initiative design framework for procedural content generation using reinforcement learning;Entertainment Computing;2025-01

2. Evaluating the Expressive Range of Super Mario Bros Level Generators;Algorithms;2024-07-11

3. Effective State Space Exploration with Phase State Graph Generation and Goal-based Path Planning;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

4. Solution Path Heuristics for Predicting Difficulty and Enjoyment Ratings of Roguelike Level Segments;Proceedings of the 19th International Conference on the Foundations of Digital Games;2024-05-21

5. Affective Game Computing: A Survey;Proceedings of the IEEE;2023-10

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