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
By:
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