Reinforcement Learning Environment with LLM-Controlled Adversary in D&D 5th Edition Combat
Project Overview
The document explores the integration of Large Language Models (LLMs) within a Reinforcement Learning (RL) framework, specifically through the lens of Dungeons & Dragons (D&D) 5E combat scenarios, to enhance strategic decision-making in educational contexts. It highlights how LLMs can contribute to AI-driven educational applications by providing deeper insights and enhancing the learning experience in complex environments. The findings reveal that although traditional RL agents have superior performance compared to LLM-controlled adversaries, the strategic depth imparted by LLMs significantly enriches the capabilities of AI in navigating dynamic and unpredictable situations. This suggests that incorporating generative AI into educational tools can foster better engagement and understanding, ultimately improving learning outcomes by leveraging the strengths of both LLMs and RL methodologies.
Key Applications
Reinforcement Learning Environment with LLM-Controlled Adversary in D&D 5E Combat
Context: Educational tool for AI development, particularly in strategic decision-making and adaptive strategies in complex environments
Implementation: Design and implementation of a reinforcement learning environment simulating D&D 5E combat using LLMs to control adversarial agents.
Outcomes: Enhanced strategic decision-making capabilities of RL agents; faster convergence and learning when trained against LLMs.
Challenges: Inconsistent responses from LLMs, slower decision-making processes compared to traditional RL agents.
Implementation Barriers
Technical Barrier
Inconsistent and unstable responses from LLMs can hinder their effectiveness in real-time strategic decision-making.
Proposed Solutions: Implementing prompt strategies to improve stability and response accuracy; integrating LLMs' responses into the RL framework.
Project Team
Joseph Emmanuel DL Dayo
Researcher
Michel Onasis S. Ogbinar
Researcher
Prospero C. Naval Jr
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Joseph Emmanuel DL Dayo, Michel Onasis S. Ogbinar, Prospero C. Naval Jr
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai