Book Image

Deep Reinforcement Learning Hands-On

By : Maxim Lapan
Book Image

Deep Reinforcement Learning Hands-On

By: Maxim Lapan

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
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The Actor-Critic (A2C) method

The first method that we'll apply to our walking robot problem is A2C, which we experimented with in part three of the book. This choice of method is quite obvious, as A2C is very easy to adapt to the continuous action domain. As a quick refresher, A2C's idea is to estimate the gradient of our policy as

. The policy is supposed to provide to us the probability distribution of actions given the observed state. The quantity is called a critic, equals to the value of the state and is trained using the Mean Square Error (MSE) between the critic return and the value estimated by the Bellman equation. To improve exploration, the entropy bonus is usually added to the loss.

Obviously, the value head of the actor-critic will be unchanged for continuous actions. The only thing that is affected is the representation of the policy. In the discrete cases that we've seen, we had only one action with several mutually exclusive discrete values. For such a case, the obvious...