1 citations · 1 across the 4 of their papers we have counts for
4 papers
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…
Prism: Spectral Parameter Sharing for Multi-Agent Reinforcement Learning
Kyungbeom Kim, Seungwon Oh, Kyung-Joong Kim
Parameter sharing is a key strategy in multi-agent reinforcement learning (MARL) for improving scalability, yet conventional fully shared architectures often collapse into homogene…
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…
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…