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

Deterministic policy gradients

The next method that we'll take a look at is called deterministic policy gradients, which is a variation of the A2C method, but has a very nice property of being off-policy. The following is my very relaxed interpretation of the strict proofs. If you are interested in understanding the core of this method deeply, you may always refer to the article by David Silver and others called Deterministic Policy Gradient Algorithms, published in 2014 and the paper by Timothy P. Lillicrap and others called Continuous Control with Deep Reinforcement Learning, published in 2015.

The simplest way to illustrate the method is by comparison with the already familiar A2C. In this method, the actor estimates the stochastic policy, which returns the probability distribution over discrete actions or, as we've just seen in the previous section, the parameters of normal distribution. In both cases, our policy was stochastic, so, in other words, our action taken was sampled from this...