Implementing the Dueling DQN agent

A Dueling DQN agent explicitly estimates two quantities through a modified network architecture:

• State values, V(s)
• Advantage values, A(s, a)

The state value estimates the value of being in state s, and the advantage value represents the advantage of taking action a in state s. This key idea of explicitly and separately estimating the two quantities enables the Dueling DQN to perform better in comparison to DQN. This recipe will walk you through the steps to implement a Dueling DQN agent from scratch using TensorFlow 2.x.

Getting ready

To complete this recipe, you will first need to activate the tf2rl-cookbook Conda Python virtual environment and pip install -r requirements.txt. If the following import statements run without issues, you are ready to get started!

import argparse
import os
import random
from collections import deque
from datetime import datetime
import gym
import numpy as np
import tensorflow as tf
from...