1 citations · 1 across the 3 of their papers we have counts for
5 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…
Multi-Objective Instruction-Aware Representation Learning in Procedural Content Generation RL
Sung-Hyun Kim, Geum-Hwan Hwang, In-Chang Baek +2
Recent advancements in generative modeling emphasize the importance of natural language as a highly expressive and accessible modality for controlling content generation. However,…
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…
Human-Aligned Procedural Level Generation Reinforcement Learning via Text-Level-Sketch Shared Representation
In-Chang Baek, Seoyoung Lee, Sung-Hyun Kim +2
Human-aligned AI is a critical component of co-creativity, as it enables models to accurately interpret human intent and generate controllable outputs that align with design goals…
IPCGRL: Language-Instructed Reinforcement Learning for Procedural Level Generation
In-Chang Baek, Sung-Hyun Kim, Seo-Young Lee +2
Recent research has highlighted the significance of natural language in enhancing the controllability of generative models. While various efforts have been made to leverage natural…