TensorFlow Agent Examples - DQN Projects

This section of the TensorFlow community focuses on tutorials and projects related to Deep Q-Networks (DQN). DQN is a popular algorithm in the field of reinforcement learning, which is used to train agents to make decisions in complex environments.

What is DQN?

Deep Q-Network (DQN) is a method for training neural networks to perform reinforcement learning tasks. It combines the power of deep learning with the concept of Q-Learning, which is a value-based method for determining the best action to take in a given state.

DQN Projects

Here are some DQN projects that you can explore:

• CartPole DQN: A classic reinforcement learning problem where an agent must balance a pole on a cart.

  • More on CartPole DQN

• Atari Breakout DQN: Train a DQN agent to play the classic Atari game "Breakout".

  • More on Atari Breakout DQN

• Mountain Car DQN: Another reinforcement learning problem where an agent must drive a car up a hill.

  • More on Mountain Car DQN

Resources

• TensorFlow Agent Examples Overview
• TensorFlow Reinforcement Learning Documentation

Community Contributions

The TensorFlow community is always looking for contributions. If you have a DQN project you'd like to share, please consider submitting it to the TensorFlow GitHub repository.

DQN Agent