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Implementing Deep Q-Learning (DQN) from Scratch Using RLax JAX Haiku and Optax to Train a CartPole Reinforcement Learning Agent

MarkTechPost / 3/23/2026

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Key Points

  • The article provides a hands-on tutorial for implementing a Deep Q-Learning DQN agent from scratch using RLax, JAX, Haiku, and Optax.
  • It demonstrates training the DQN agent to solve the CartPole environment, illustrating practical reinforcement learning workflow.
  • It emphasizes a from-scratch approach without a fully packaged RL framework, focusing on core components like networks, replay buffer, and target updates.
  • It shows how RLax integrates with the broader JAX ecosystem, with Haiku for neural networks and Optax for optimization.

In this tutorial, we implement a reinforcement learning agent using RLax, a research-oriented library developed by Google DeepMind for building reinforcement learning algorithms with JAX. We combine RLax with JAX, Haiku, and Optax to construct a Deep Q-Learning (DQN) agent that learns to solve the CartPole environment. Instead of using a fully packaged RL framework, […]

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