Implementing Deep Reinforcement Learning Models with Tensorflow + OpenAI Gym
Lilian Weng
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.
Why it matters
The full implementation is available in lilianweng/deep-reinforcement-learning-gym In the previous two posts, I have introduced the algorithms of many deep reinforcement learning models. Now it is the time to get our hands dirty and practice how to implement the models in the wild. The implementation is gonna be built in Tensorflow and OpenAI gym environment. The full version of the code in this tutorial is available in [lilian/deep-reinforcement-learning-gym].
