Book Image

Deep Reinforcement Learning Hands-On - Second Edition

By : Maxim Lapan
5 (2)
Book Image

Deep Reinforcement Learning Hands-On - Second Edition

5 (2)
By: Maxim Lapan

Overview of this book

Deep Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques. It provides you with an introduction to the fundamentals of RL, along with the hands-on ability to code intelligent learning agents to perform a range of practical tasks. With six new chapters devoted to a variety of up-to-the-minute developments in RL, including discrete optimization (solving the Rubik's Cube), multi-agent methods, Microsoft's TextWorld environment, advanced exploration techniques, and more, you will come away from this book with a deep understanding of the latest innovations in this emerging field. In addition, you will gain actionable insights into such topic areas as deep Q-networks, policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. You will also discover how to build a real hardware robot trained with RL for less than $100 and solve the Pong environment in just 30 minutes of training using step-by-step code optimization. In short, Deep Reinforcement Learning Hands-On, Second Edition, is your companion to navigating the exciting complexities of RL as it helps you attain experience and knowledge through real-world examples.
Table of Contents (28 chapters)
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A2C on Pong

In the previous chapter, you saw a (not very successful) attempt to solve our favorite Pong environment with policy gradient methods. Let's try it again with the actor-critic method at hand.

GAMMA = 0.99

We start, as usual, by defining hyperparameters (imports are omitted). These values are not tuned, as we will do this in the next section of this chapter. We have one new value here: CLIP_GRAD. This hyperparameter specifies the threshold for gradient clipping, which basically prevents our gradients from becoming too large at the optimization stage and pushing our policy too far. Clipping is implemented using the PyTorch functionality, but the idea is very simple: if the L2 norm of the gradient is larger than this hyperparameter, then the gradient vector is clipped to this value.

The REWARD_STEPS hyperparameter determines how many...