#### 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.
Preface
Free Chapter
Introduction to Reinforcement Learning
Getting Started with OpenAI and TensorFlow
The Markov Decision Process and Dynamic Programming
Gaming with Monte Carlo Methods
Temporal Difference Learning
Multi-Armed Bandit Problem
Deep Learning Fundamentals
Atari Games with Deep Q Network
Playing Doom with a Deep Recurrent Q Network
The Asynchronous Advantage Actor Critic Network
Capstone Project – Car Racing Using DQN
Assessments
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# TD prediction

Like we did in Monte Carlo prediction, in TD prediction we try to predict the state values. In Monte Carlo prediction, we estimate the value function by simply taking the mean return. But in TD learning, we update the value of a previous state by current state. How can we do this? TD learning using something called a TD update rule for updating the value of a state, as follows:

The value of a previous state = value of previous state + learning_rate (reward + discount_factor(value of current state) - value of previous state)

What does this equation actually mean?

If you think of this equation intuitively, it is actually the difference between the actual reward () and the expected reward () multiplied by the learning rate alpha. What does the learning rate signify? The learning rate, also called step size, is useful for convergence.

Did you notice? Since we take the...