Reinforcement Learning Environment with LLM-Controlled Adversary in D&D 5th Edition Combat

• URL: http://arxiv.org/abs/2503.15726v1
• Date: Wed, 19 Mar 2025 22:48:20 GMT
• Title: Reinforcement Learning Environment with LLM-Controlled Adversary in D&D 5th Edition Combat
• Authors: Joseph Emmanuel DL Dayo, Michel Onasis S. Ogbinar, Prospero C. Naval Jr,
• Abstract summary: This research employs Deep Q-Networks (DQN) for the smaller agents, creating a testbed for strategic AI development.<n>We successfully integrated sophisticated language models into the RL framework, enhancing strategic decision-making processes.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The objective of this study is to design and implement a reinforcement learning (RL) environment using D\&D 5E combat scenarios to challenge smaller RL agents through interaction with a robust adversarial agent controlled by advanced Large Language Models (LLMs) like GPT-4o and LLaMA 3 8B. This research employs Deep Q-Networks (DQN) for the smaller agents, creating a testbed for strategic AI development that also serves as an educational tool by simulating dynamic and unpredictable combat scenarios. We successfully integrated sophisticated language models into the RL framework, enhancing strategic decision-making processes. Our results indicate that while RL agents generally outperform LLM-controlled adversaries in standard metrics, the strategic depth provided by LLMs significantly enhances the overall AI capabilities in this complex, rule-based setting. The novelty of our approach and its implications for mastering intricate environments and developing adaptive strategies are discussed, alongside potential innovations in AI-driven interactive simulations. This paper aims to demonstrate how integrating LLMs can create more robust and adaptable AI systems, providing valuable insights for further research and educational applications.

Related papers

• Agentic Reinforced Policy Optimization [66.96989268893932]
  Large-scale reinforcement learning with verifiable rewards (RLVR) has demonstrated its effectiveness in harnessing the potential of large language models (LLMs) for single-turn reasoning tasks.<n>Current RL algorithms inadequately balance the models' intrinsic long-horizon reasoning capabilities and their proficiency in multi-turn tool interactions.<n>We propose Agentic Reinforced Policy Optimization (ARPO), a novel agentic RL algorithm tailored for training multi-turn LLM-based agents.
  arXiv  Detail & Related papers  (2025-07-26T07:53:11Z)
• Omni-Thinker: Scaling Cross-Domain Generalization in LLMs via Multi-Task RL with Hybrid Rewards [50.21528417884747]
  We introduce Omni-Thinker, a unified reinforcement learning framework that enhances large language models (LLMs) performance across diverse tasks.<n>Our approach enables consistent optimization across task types and scales RL-based training to subjective domains.<n> Experimental results across four domains reveal that curriculum learning improves performance by 5.2% over joint training and 9.1% over model merging.
  arXiv  Detail & Related papers  (2025-07-20T01:50:16Z)
• Mastering Da Vinci Code: A Comparative Study of Transformer, LLM, and PPO-based Agents [0.0]
  The Da Vinci Code, a game of logical deduction and imperfect information, presents unique challenges for artificial intelligence.<n>This paper investigates the efficacy of various AI paradigms in mastering this game.
  arXiv  Detail & Related papers  (2025-06-15T10:33:30Z)
• ReMA: Learning to Meta-think for LLMs with Multi-Agent Reinforcement Learning [54.787341008881036]
  We introduce Reinforced Meta-thinking Agents (ReMA), a novel framework that leverages Multi-Agent Reinforcement Learning (MARL) to elicit meta-thinking behaviors.<n>ReMA decouples the reasoning process into two hierarchical agents: a high-level meta-thinking agent responsible for generating strategic oversight and plans, and a low-level reasoning agent for detailed executions.<n> Experimental results demonstrate that ReMA outperforms single-agent RL baselines on complex reasoning tasks.
  arXiv  Detail & Related papers  (2025-03-12T16:05:31Z)
• An Autonomous Network Orchestration Framework Integrating Large Language Models with Continual Reinforcement Learning [13.3347292702828]
  This paper proposes a framework called Autonomous Reinforcement Coordination (ARC) for a SemCom-enabled SAGIN.<n>ARC decomposes orchestration into two tiers, utilizing LLMs for high-level planning and RL agents for low-level decision-making.
  arXiv  Detail & Related papers  (2025-02-22T11:53:34Z)
• EPO: Explicit Policy Optimization for Strategic Reasoning in LLMs via Reinforcement Learning [69.55982246413046]
  We propose explicit policy optimization (EPO) for strategic reasoning.<n>EPO provides strategies in open-ended action space and can be plugged into arbitrary LLM agents to motivate goal-directed behavior.<n> Experiments across social and physical domains demonstrate EPO's ability of long-term goal alignment.
  arXiv  Detail & Related papers  (2025-02-18T03:15:55Z)
• Scaling Autonomous Agents via Automatic Reward Modeling And Planning [52.39395405893965]
  Large language models (LLMs) have demonstrated remarkable capabilities across a range of tasks. However, they still struggle with problems requiring multi-step decision-making and environmental feedback. We propose a framework that can automatically learn a reward model from the environment without human annotations.
  arXiv  Detail & Related papers  (2025-02-17T18:49:25Z)
• TeLL-Drive: Enhancing Autonomous Driving with Teacher LLM-Guided Deep Reinforcement Learning [61.33599727106222]
  TeLL-Drive is a hybrid framework that integrates a Teacher LLM to guide an attention-based Student DRL policy.<n>A self-attention mechanism then fuses these strategies with the DRL agent's exploration, accelerating policy convergence and boosting robustness.
  arXiv  Detail & Related papers  (2025-02-03T14:22:03Z)
• Learning Autonomous Code Integration for Math Language Models [30.057052324461534]
  We propose a novel framework that synergizes structured exploration (E-step) with off-policy optimization (M-step) to create a self-reinforcing cycle between metacognitive tool-use decisions and evolving capabilities.<n>Our 7B model improves over 11% on MATH500 and 9.4% on AIME without o1-like CoT.
  arXiv  Detail & Related papers  (2025-02-02T06:32:23Z)
• A Survey on Large Language Model-empowered Autonomous Driving [25.963195890376646]
  Development of autonomous driving (AD) technology follows two main technical paths: modularization and end-to-end. This paper conducts a thorough analysis of the potential applications of large language models (LLMs) in AD systems. We discuss an important question: Can LLM-based artificial general intelligence (AGI) be a key to achieve high-level AD?
  arXiv  Detail & Related papers  (2024-09-21T15:07:37Z)
• Mastering the Digital Art of War: Developing Intelligent Combat Simulation Agents for Wargaming Using Hierarchical Reinforcement Learning [0.0]
  dissertation proposes a comprehensive approach, including targeted observation abstractions, multi-model integration, a hybrid AI framework, and an overarching hierarchical reinforcement learning framework. Our localized observation abstraction using piecewise linear spatial decay simplifies the RL problem, enhancing computational efficiency and demonstrating superior efficacy over traditional global observation methods. Our hybrid AI framework synergizes RL with scripted agents, leveraging RL for high-level decisions and scripted agents for lower-level tasks, enhancing adaptability, reliability, and performance.
  arXiv  Detail & Related papers  (2024-08-23T18:50:57Z)
• Strategist: Self-improvement of LLM Decision Making via Bi-Level Tree Search [32.657454056329875]
  Large language models (LLMs) exhibit strong generalization and zero-shot capabilities, but struggle with tasks that require detailed planning and decision-making.<n>We introduce STRATEGIST, a novel approach that integrates the strengths of both methods.<n>We demonstrate the effectiveness of STRATEGIST in learning optimal strategies for competitive, multi-turn games with partial information.
  arXiv  Detail & Related papers  (2024-08-20T08:22:04Z)
• Retrieval-Augmented Hierarchical in-Context Reinforcement Learning and Hindsight Modular Reflections for Task Planning with LLMs [8.55917897789612]
  We propose Retrieval-Augmented in-context reinforcement Learning (RAHL) for large language models. RAHL decomposes complex tasks into sub-tasks using an LLM-based high-level policy. We show that RAHL can achieve an improvement in performance in 9%, 42%, and 10% in 5 episodes of execution in strong baselines.
  arXiv  Detail & Related papers  (2024-08-12T22:40:01Z)
• Aquatic Navigation: A Challenging Benchmark for Deep Reinforcement Learning [53.3760591018817]
  We propose a new benchmarking environment for aquatic navigation using recent advances in the integration between game engines and Deep Reinforcement Learning. Specifically, we focus on PPO, one of the most widely accepted algorithms, and we propose advanced training techniques. Our empirical evaluation shows that a well-designed combination of these ingredients can achieve promising results.
  arXiv  Detail & Related papers  (2024-05-30T23:20:23Z)
• Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents [101.17919953243107]
  GovSim is a generative simulation platform designed to study strategic interactions and cooperative decision-making in large language models (LLMs)<n>We find that all but the most powerful LLM agents fail to achieve a sustainable equilibrium in GovSim, with the highest survival rate below 54%.<n>We show that agents that leverage "Universalization"-based reasoning, a theory of moral thinking, are able to achieve significantly better sustainability.
  arXiv  Detail & Related papers  (2024-04-25T15:59:16Z)
• Put Your Money Where Your Mouth Is: Evaluating Strategic Planning and Execution of LLM Agents in an Auction Arena [25.865825113847404]
  We introduce AucArena, a novel evaluation suite that simulates auctions. We conduct controlled experiments using state-of-the-art Large Language Models (LLMs) to power bidding agents to benchmark their planning and execution skills.
  arXiv  Detail & Related papers  (2023-10-09T14:22:09Z)
• LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving [84.31119464141631]
  This work employs Large Language Models (LLMs) as a decision-making component for complex autonomous driving scenarios. Extensive experiments demonstrate that our proposed method not only consistently surpasses baseline approaches in single-vehicle tasks, but also helps handle complex driving behaviors even multi-vehicle coordination.
  arXiv  Detail & Related papers  (2023-10-04T17:59:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.