#### Overview of this book

Reinforcement learning has evolved a lot in the last couple of years and proven to be a successful technique in building smart and intelligent AI networks. Keras Reinforcement Learning Projects installs human-level performance into your applications using algorithms and techniques of reinforcement learning, coupled with Keras, a faster experimental library. The book begins with getting you up and running with the concepts of reinforcement learning using Keras. You’ll learn how to simulate a random walk using Markov chains and select the best portfolio using dynamic programming (DP) and Python. You’ll also explore projects such as forecasting stock prices using Monte Carlo methods, delivering vehicle routing application using Temporal Distance (TD) learning algorithms, and balancing a Rotating Mechanical System using Markov decision processes. Once you’ve understood the basics, you’ll move on to Modeling of a Segway, running a robot control system using deep reinforcement learning, and building a handwritten digit recognition model in Python using an image dataset. Finally, you’ll excel in playing the board game Go with the help of Q-Learning and reinforcement learning algorithms. By the end of this book, you’ll not only have developed hands-on training on concepts, algorithms, and techniques of reinforcement learning but also be all set to explore the world of AI.
Table of Contents (13 chapters)
Preface
Free Chapter
Overview of Keras Reinforcement Learning
Simulating Random Walks
Optimal Portfolio Selection
Forecasting Stock Market Prices
Delivery Vehicle Routing Application
Continuous Balancing of a Rotating Mechanical System
Dynamic Modeling of a Segway as an Inverted Pendulum System
Robot Control System Using Deep Reinforcement Learning
Handwritten Digit Recognizer
Playing the Board Game Go
What's Next?
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# Continuous control with deep reinforcement learning

In this example, we will address the problem of an inverted pendulum swinging up—this is a classic problem in control theory. In this version of the problem, the pendulum starts in a random position, and the goal is to swing it up so that it stays upright. Torque limits prevent the agent from swinging the pendulum up directly. The following diagram shows the problem:

The problem is addressed using an environment available in the OpenAI Gym library (Pendulum-v0) with the help of the DDPG agent of the keras-rl library (DDPGAgent).

OpenAI Gym is a library that helps us to implement algorithms based on reinforcement learning. It includes a growing collection of benchmark issues that expose a common interface and a website where people can share their results and compare algorithm performance. For the moment, we will imitate...