Reinforcement Learning (RL) agents often struggle with efficiency and performance in complex environments. We propose a novel framework that uses a Large Language Model (LLM) to dynamically generate a curriculum over available actions, enabling the agent to incorporate each action individually. We apply this framework to the game of Blackjack, where the LLM creates a multi-stage training path that progressively introduces complex actions to a Tabular Q-Learning and a Deep Q-Network (DQN) agent. Our evaluation in a realistic 8-deck simulation over 10 independent runs demonstrates significant performance gains over standard training methods. The curriculum-based approach increases the DQN agent's average win rate from 43.97% to 47.41%, reduces the average bust rate from 32.9% to 28.0%, and accelerates the overall workflow by over 74%, with the agent's full training completing faster than the baseline's evaluation phase alone. These results validate that LLM-guided curricula can build more effective, robust, and efficient RL agents.

翻译：强化学习（RL）智能体在复杂环境中常面临效率与性能的挑战。我们提出了一种新颖框架，利用大语言模型（LLM）动态生成可用动作的课程体系，使智能体能够逐步掌握每个动作。我们将该框架应用于二十一点游戏，通过LLM构建多阶段训练路径，逐步向表格型Q学习和深度Q网络（DQN）智能体引入复杂动作。在基于10次独立运行的8副牌真实模拟评估中，该方法相比标准训练方法展现出显著性能提升。课程学习方法使DQN智能体的平均胜率从43.97%提升至47.41%，平均爆牌率从32.9%降至28.0%，并将整体工作流程加速超过74%（智能体完整训练时间少于基准方法的单次评估阶段）。这些结果验证了LLM引导的课程体系能够构建更高效、更鲁棒的强化学习智能体。