ChatPCG: Large Language Model-Driven Reward Design for Procedural Content Generation
Driven by the rapid growth of machine learning, recent advances in game artificial intelligence (AI) have significantly impacted productivity across various gaming genres. Reward design plays a pivotal role in training game AI models, wherein researchers implement concepts of specific reward functions. However, despite the presence of AI, the reward design process predominantly remains in the domain of human experts, as it is heavily reliant on their creativity and engineering skills. Therefore, this paper proposes ChatPCG, a large language model (LLM)-driven reward design framework.It leverages human-level insights, coupled with game expertise, to generate rewards tailored to specific game features automatically. Moreover, ChatPCG is integrated with deep reinforcement learning, demonstrating its potential for multiplayer game content generation tasks. The results suggest that the proposed LLM exhibits the capability to comprehend game mechanics and content generation tasks, enabling tailored content generation for a specified game. This study not only highlights the potential for improving accessibility in content generation but also aims to streamline the game AI development process.
Code (0)
등록된 구현이 없습니다.
Tasks
Deep Reinforcement LearningLanguage ModelingLanguage ModellingLarge Language ModelSimilar Papers 제목 키워드 기반
Large Language Model-Based Reward Design for Deep Reinforcement Learning-Driven Autonomous Cyber Defense
Designing rewards for autonomous cyber attack and defense learning agents in a complex, dynamic environment is a challenging task for subject matter experts. We propose a large language model (LLM)-based reward design ap…
Reinforcement LearningA Large Language Model-Driven Reward Design Framework via Dynamic Feedback for Reinforcement Learning
Large Language Models (LLMs) have shown significant potential in designing reward functions for Reinforcement Learning (RL) tasks. However, obtaining high-quality reward code often involves human intervention, numerous L…
Language ModelingLanguage ModellingLarge Language ModelReinforcement Learning (RL)Causal Reward World Models: Zero-shot Reward Design for Automated Skill Generation
Automated Reward Design (ARD) aims to replace manual reward engineering in reinforcement learning with language-driven reward function synthesis. However, existing approaches based on large language models (LLMs) remain …
Reinforcement LearningContinuous ControlEnhanced LLM Reasoning by Optimizing Reward Functions with Search-Driven Reinforcement Learning
Mathematical reasoning is a key benchmark for large language models. Reinforcement learning is a standard post-training mechanism for improving the reasoning capabilities of large language models, yet performance remains…
Reinforcement LearningMathematical ReasoningGRPO-TTA: Test-Time Visual Tuning for Vision-Language Models via GRPO-Driven Reinforcement Learning
Group Relative Policy Optimization (GRPO) has recently shown strong performance in post-training large language models and vision-language models. It raises a question of whether the GRPO also significantly promotes the …
Reinforcement LearningTest-time Adaptation