Implementing Deep Reinforcement Learning Models with Tensorflow + OpenAI Gym

Lilian Weng · 8 years ago

This article provides a tutorial on implementing deep reinforcement learning models using TensorFlow and OpenAI Gym, covering Q-learning, Deep Q-Networks, Double Q-Learning, and Dueling Q-Networks with code examples. The implementation uses a batch size of 32 transitions and a discount factor (gamma) of 0.99 for training stability. The tutorial demonstrates how these algorithms progress from simple Q-learning to more sophisticated neural network-based approaches that reduce overestimation bias and improve training efficiency.