Learning to Play Blackjack: A Curriculum Learning Perspective
Summary: arXiv:2604.00076v1 Announce Type: cross
Abstract
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.
Introduction
The study of Reinforcement Learning (RL) has gained immense traction in recent years, particularly in
complex game environments such as Blackjack. Traditional RL methods have faced challenges in achieving
optimal performance due to the intricate nature of the actions involved. This article outlines a
groundbreaking approach that leverages Large Language Models (LLMs) to enhance the training of RL agents
through a structured curriculum.
Methodology
Our proposed framework utilizes an LLM to construct a multi-stage training path that introduces
increasingly complex actions to both a Tabular Q-Learning agent and a Deep Q-Network (DQN) agent.
The curriculum is designed to systematically build the agent’s understanding of the game,
allowing for a more focused and efficient learning process.
Results
We evaluated our framework in a realistic 8-deck Blackjack simulation over 10 independent runs.
The results demonstrated significant improvements compared to standard training methods.
- The DQN agent’s average win rate increased from 43.97% to 47.41%.
- The average bust rate was reduced from 32.9% to 28.0%.
- The overall training workflow was accelerated by over 74%.
Notably, the DQN agent’s full training was completed faster than the baseline’s evaluation phase alone.
These findings suggest that LLM-guided curricula can significantly enhance the performance and efficiency
of RL agents.
Conclusion
The integration of Large Language Models into the training of reinforcement learning agents opens new avenues
for developing more effective and robust systems. Our study highlights the potential of curriculum learning in
complex environments, providing a promising direction for future research in the field. The results validate
that the systematic introduction of actions can lead to substantial improvements in both performance and
training efficiency, making it a valuable approach for various applications beyond just gaming.
Future Work
As we look ahead, further research is needed to explore the applicability of LLM-guided curricula in
other complex environments. Additionally, investigating the scalability of this approach and its
integration with other RL methodologies could yield exciting developments in artificial intelligence.