Most of the environments that include continuous action spaces are related to the physical world, so physics simulations are normally used. There are lots of software packages that can simulate physical processes, from very simple, open-source tools to complex, commercial packages that can simulate multiphysics processes (such as fluid, burning, and strength simulations). In the case of robotics, one of the most popular packages is MuJoCo, which stands for **Multi-Joint Dynamics with Contact** (www.mujoco.org). This is a physics engine in which you can define the components of the system, their interaction and properties. Then the simulator is responsible for *solving the system* by taking into account your intervention and finding the parameters (usually the location, velocities, and accelerations) of the components. This makes it ideal as a playground for RL environments, as you can define fairly complicated systems (such as multipede robots or robotic arms or humanoids) and then...

#### Deep Reinforcement Learning Hands-On

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#### Deep Reinforcement Learning Hands-On

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#### Overview of this book

Deep Reinforcement Learning Hands-On is a comprehensive guide to the very latest DL tools and their limitations. You will evaluate methods including Cross-entropy and policy gradients, before applying them to real-world environments. Take on both the Atari set of virtual games and family favorites such as Connect4.
The book provides an introduction to the basics of RL, giving you the know-how to code intelligent learning agents to take on a formidable array of practical tasks. Discover how to implement Q-learning on 'grid world' environments, teach your agent to buy and trade stocks, and find out how natural language models are driving the boom in chatbots.

Table of Contents (23 chapters)

Deep Reinforcement Learning Hands-On

Contributors

Preface

Other Books You May Enjoy

Free Chapter

What is Reinforcement Learning?

OpenAI Gym

Deep Learning with PyTorch

The Cross-Entropy Method

Tabular Learning and the Bellman Equation

Deep Q-Networks

DQN Extensions

Stocks Trading Using RL

Policy Gradients – An Alternative

The Actor-Critic Method

Asynchronous Advantage Actor-Critic

Chatbots Training with RL

Web Navigation

Continuous Action Space

Trust Regions – TRPO, PPO, and ACKTR

Black-Box Optimization in RL

Beyond Model-Free – Imagination

AlphaGo Zero

Index

Customer Reviews