Multi-Objective Instruction-Aware Representation Learning in Procedural Content Generation RL

2025-08-08

AI Summary

Existing instruction-based reinforcement learning approaches for interactive procedural content generation (IPCGRL) struggle to adequately model complex, multi-objective natural language instructions, resulting in insufficient generation controllability. To address this, we propose MIPCGRL—a framework that introduces an instruction-aware multi-objective representation learning mechanism. Specifically, it employs a joint neural architecture integrating multi-label classification and multi-head regression to map sentence embeddings into a structured multi-objective embedding space, enabling fine-grained disentanglement and synergistic modeling of instruction semantics. This design significantly enhances the model’s comprehension and responsiveness to multi-objective constraints. Experimental results demonstrate that, under multi-objective instruction settings, MIPCGRL improves generation controllability by up to 13.8% over baselines, enabling more precise, flexible, and semantically consistent content generation.

Technology Category

• Natural Language Processing: Generation
• Machine Learning: Multi-instance/Multi-view Learning
• Humans and AI: Game Design — Procedural Content Generation & Storytelling

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

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. To address this problem, we propose extit{MIPCGRL}, a multi-objective representation learning method for instructed content generators, which incorporates sentence embeddings as conditions. MIPCGRL effectively trains a multi-objective embedding space by incorporating multi-label classification and multi-head regression networks. Experimental results show that the proposed method achieves up to a 13.8% improvement in controllability with multi-objective instructions. The ability to process complex instructions enables more expressive and flexible content generation.

Problem

• Leveraging textual input richness in instructed RL for content generation
• Improving controllability under complex multi-objective instructions
• Enhancing expressive flexibility in procedural content generation

Innovation

• Uses sentence embeddings as conditions
• Trains multi-objective embedding space
• Improves controllability with multi-objective instructions