Book Image

Python Reinforcement Learning Projects

By : Sean Saito, Yang Wenzhuo, Rajalingappaa Shanmugamani
Book Image

Python Reinforcement Learning Projects

By: Sean Saito, Yang Wenzhuo, Rajalingappaa Shanmugamani

Overview of this book

Reinforcement learning is one of the most exciting and rapidly growing fields in machine learning. This is due to the many novel algorithms developed and incredible results published in recent years. In this book, you will learn about the core concepts of RL including Q-learning, policy gradients, Monte Carlo processes, and several deep reinforcement learning algorithms. As you make your way through the book, you'll work on projects with datasets of various modalities including image, text, and video. You will gain experience in several domains, including gaming, image processing, and physical simulations. You'll explore technologies such as TensorFlow and OpenAI Gym to implement deep learning reinforcement learning algorithms that also predict stock prices, generate natural language, and even build other neural networks. By the end of this book, you will have hands-on experience with eight reinforcement learning projects, each addressing different topics and/or algorithms. We hope these practical exercises will provide you with better intuition and insight about the field of reinforcement learning and how to apply its algorithms to various problems in real life.
Table of Contents (17 chapters)
Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
Index

Deterministic policy gradient


As discussed in the previous chapter, DQN uses the Q-network to estimate the state-action value function, which has a separate output for each available action. Therefore, the Q-network cannot be applied, due to the continuous action space. A careful reader may remember that there is another architecture of the Q-network that takes both the state and the action as its inputs, and outputs the estimate of the corresponding Q-value. This architecture doesn't require the number of available actions to be finite, and has the capability to deal with continuous input actions:

If we use this kind of network to estimate the state-action value function, there must be another network that defines the behavior policy of the agent, namely outputting a proper action given the observed state. In fact, this is the intuition behind actor-critic reinforcement learning algorithms. The actor-critic architecture contains two parts:

  1. Actor: The actor defines the behavior policy of the...