7 papers
PCGRLLM: Large Language Model-Driven Reward Design for Procedural Content Generation Reinforcement Learning
In-Chang Baek, Sung-Hyun Kim, Sam Earle +4
Reward design plays a pivotal role in the training of game AIs, requiring substantial domain-specific knowledge and human effort. In recent years, several studies have explored rew…
A Markovian Framing of WaveFunctionCollapse for Procedurally Generating Aesthetically Complex Environments
Franklin Yiu, Mohan Lu, Nina Li +5
Procedural content generation often requires satisfying both designer-specified objectives and adjacency constraints implicitly imposed by the underlying tile set. To address the c…
Video Game Level Design as a Multi-Agent Reinforcement Learning Problem
Sam Earle, Zehua Jiang, Eugene Vinitsky +1
Procedural Content Generation via Reinforcement Learning (PCGRL) offers a method for training controllable level designer agents without the need for human datasets, using metrics…
All Stories Are One Story: Emotional Arc Guided Procedural Game Level Generation
Yunge Wen, Chenliang Huang, Hangyu Zhou +5
The emotional arc is a universal narrative structure underlying stories across cultures and media -- an idea central to structuralist narratology, often encapsulated in the phrase…
Enhancing Player Enjoyment with a Two-Tier DRL and LLM-Based Agent System for Fighting Games
Shouren Wang, Zehua Jiang, Fernando Sliva +2
Deep reinforcement learning (DRL) has effectively enhanced gameplay experiences and game design across various game genres. However, few studies on fighting game agents have focuse…
Amorphous Fortress Online: Collaboratively Designing Open-Ended Multi-Agent AI and Game Environments
M Charity, Mayu Wilson, Steven Lee +3
This work introduces Amorphous Fortress Online -- a web-based platform where users can design petri-dish-like environments and games consisting of multi-agent AI characters. Users…