1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.AI2026★ 1 cited
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…
cs.AI2026
Multiverse: Language-Conditioned Multi-Game Level Blending via Shared Representation
In-Chang Baek, Jiyun Jung, Geum-Hwan Hwang +2
Text-to-level generation aims to translate natural language descriptions into structured game levels, enabling intuitive control over procedural content generation. While prior tex…
cs.AI2024
ChatPCG: Large Language Model-Driven Reward Design for Procedural Content Generation
In-Chang Baek, Tae-Hwa Park, Jin-Ha Noh +2
Driven by the rapid growth of machine learning, recent advances in game artificial intelligence (AI) have significantly impacted productivity across various gaming genres. Reward d…