13 citations · 14 across the 8 of their papers we have counts for
4 papers · 1 filter
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…
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,…
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…
Automatic Curriculum Design for Zero-Shot Human-AI Coordination
Won-Sang You, Tae-Gwan Ha, Seo-Young Lee +1
Zero-shot human-AI coordination is the training of an ego-agent to coordinate with humans without human data. Most studies on zero-shot human-AI coordination have focused on enhanc…