Book Image

Hands-On Reinforcement Learning with Python

By : Sudharsan Ravichandiran
Book Image

Hands-On Reinforcement Learning with Python

By: Sudharsan Ravichandiran

Overview of this book

Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. Hands-On Reinforcement learning with Python will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms. The book starts with an introduction to Reinforcement Learning followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms and concepts, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. This example-rich guide will introduce you to deep reinforcement learning algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many more of the recent advancements in reinforcement learning. By the end of the book, you will have all the knowledge and experience needed to implement reinforcement learning and deep reinforcement learning in your projects, and you will be all set to enter the world of artificial intelligence.
Table of Contents (16 chapters)

Training the network

Now, we will see how to train the network.

First, we define the DQN class and initialize all variables in the __init__ method:

class DQN(object):
def __init__(self, state_size,
summary_writer = None,
exploration_period = 1000,
minibatch_size = 32,
discount_factor = 0.99,
experience_replay_buffer = 10000,
target_qnet_update_frequency = 10000,
initial_exploration_epsilon = 1.0,
final_exploration_epsilon = 0.05,
reward_clipping = -1,

Initialize all variables:

self.state_size = state_size
self.action_size = action_size

self.session = session