keon/deep-q-learning resource

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1328 stars · 454 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A minimal, under-100-lines implementation of Deep Q-Learning (DQN and Double DQN) in Python using Keras and Gymnasium, built as a companion to a tutorial blog post. It trains a game-playing AI agent on CartPole with experience replay and save/load helpers.

Use cases

• learn deep q-learning from a small readable codebase
• implement a DQN agent in Keras
• train an AI to play CartPole with reinforcement learning
• understand the difference between DQN and Double DQN
• see how experience replay works in practice
• find a minimal starting point for deep RL experiments

When to choose

• you want a tiny, educational DQN/DDQN reference implementation
• you are learning deep reinforcement learning and prefer minimal code over frameworks
• you need a Keras-based Q-learning example updated for modern Keras 3 and Gymnasium

When to avoid

• you need a production-grade or feature-rich RL library with many algorithms
• you require GPU-optimized, scalable, or distributed RL training
• you need support for complex environments beyond simple Gymnasium tasks

Facets

learning-resource · maturity active

reinforcement-learning deep-learning machine-learning reinforcement-learning deep-learning machine-learning tutorials education python dqn ddqn keras gymnasium cartpole experience-replay q-learning educational-code

Member repositories

For agents

markdown · JSON · MCP: product_card(name="keon/deep-q-learning")