Building an agent to play Atari games

Now we will see how to build an agent to play any Atari game. You can get the complete code as a Jupyter notebook with the explanation here (https://github.com/sudharsan13296/Hands-On-Reinforcement-Learning-With-Python/blob/master/08.%20Atari%20Games%20with%20DQN/8.8%20Building%20an%20Agent%20to%20Play%20Atari%20Games.ipynb).

First, we import all the necessary libraries:

import numpy as np
import gym
import tensorflow as tf
from tensorflow.contrib.layers import flatten, conv2d, fully_connected
from collections import deque, Counter
import random
from datetime import datetime

We can use any of the Atari gaming environments given here: http://gym.openai.com/envs/#atari.

In this example, we use the Pac-Man game environment:

env = gym.make("MsPacman-v0")
n_outputs = env.action_space.n

The Pac-Man environment is shown here:

Now we define a preprocess_observation function for preprocessing our input game screen. We reduce the image size and convert the image to grayscale...