AI RESEARCH
Multi-Objective Instruction-Aware Representation Learning in Procedural Content Generation RL
arXiv CS.LG
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ArXi:2508.09193v2 Announce Type: replace Recent advancements in generative modeling emphasize the importance of natural language as a highly expressive and accessible modality for controlling content generation. However, existing instructed reinforcement learning for procedural content generation (IPCGRL) method often struggle to leverage the expressive richness of textual input, especially under complex, multi-objective instructions, leading to limited controllability.