Playing Connect Four with Deep Q-Learning

2026-05-04 · Source: Towards Data Science · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Data Science & Analytics · Depth: Intermediate, medium

Summary

This article details the application of Deep Q-Learning (DQN) to the multi-player game Connect Four, addressing limitations of tabular and approximate Sarsa methods in complex environments. The approach transitions from online updates to a batched training setup using a replay buffer, moving from on-policy Sarsa to off-policy Q-learning. Key enhancements include a vectorized environment wrapper for parallel game simulation, achieving 50–100 games per second throughput. The DQN agent, trained as a pool of evolving agents, significantly outperforms a random policy, reducing its win rate from 50% to approximately 20% after one million steps. While effective offensively, the agent demonstrates weaknesses in defensive play, highlighting challenges in non-stationary multi-player settings.

Key takeaway

For AI Engineers developing agents for complex multi-player games, adopting Deep Q-Learning with replay buffers and batched updates is crucial for scalability and performance. Your focus should shift from general frameworks to specialized, efficient implementations to overcome limitations in defensive play and non-stationary environments, especially when aiming to surpass human-level performance.

Key insights

Deep Q-Learning with batched updates and replay buffers enables effective policy learning in complex multi-player environments.

Principles

• Off-policy learning stabilizes training.
• Batched updates improve computational efficiency.
• Environment vectorization increases throughput.

Method

Deep Q-Learning uses a neural network $Q_\theta(s,a)$ to approximate the action-value function, minimizing Huber loss between predicted Q-values and bootstrapped targets derived from rewards and discounted next-state values, with legal action masking.

In practice

• Implement replay buffers for experience storage.
• Use vectorized environments for parallel simulation.
• Apply action masking for games with illegal moves.

Topics

• Deep Q-Learning
• Connect Four
• Reinforcement Learning
• Replay Buffer
• Batched Training

Code references

• hermanmichaels/rl_book

Best for: Machine Learning Engineer, AI Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards Data Science.