Implementing the Deep Q-Learning algorithm, DQN, and Double-DQN agent

DQN agent uses a deep neural network to learn the Q-value function. DQN has shown itself to be a powerful algorithm for discrete action-space environments and problems and is considered to be a notable milestone in the history of deep reinforcement learning when DQN mastered Atari Games.

The Double-DQN agent uses two identical deep neural networks that are updated differently and so hold different weights. The second neural network is a copy of the main neural network from some time in the past (typically from the last episode).

By the end of this recipe, you will have implemented a complete DQN and Double-DQN agent from scratch using TensorFlow 2.x that is ready to be trained in any discrete action-space RL environment.

Let's get started.

Getting ready

To complete this recipe, you will first need to activate the tf2rl-cookbook Conda Python virtual environment and pip install -r requirements...