Book Image

Deep Reinforcement Learning Hands-On - Second Edition

By : Maxim Lapan
5 (1)
Book Image

Deep Reinforcement Learning Hands-On - Second Edition

5 (1)
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)
26
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27
Index

Policy gradient methods on CartPole

Nowadays, almost nobody uses the vanilla policy gradient method, as the much more stable actor-critic method exists. However, I still want to show the policy gradient implementation, as it establishes very important concepts and metrics to check the policy gradient method's performance.

Implementation

So, we will start with a much simpler environment of CartPole, and in the next section, we will check its performance on our favorite Pong environment.

The complete code for the following example is available in Chapter11/04_cartpole_pg.py.

GAMMA = 0.99
LEARNING_RATE = 0.001
ENTROPY_BETA = 0.01
BATCH_SIZE = 8
REWARD_STEPS = 10

Besides the already familiar hyperparameters, we have two new ones: the ENTROPY_BETA value is the scale of the entropy bonus and the REWARD_STEPS value specifies how many steps ahead the Bellman equation is unrolled to estimate the discounted total reward of every transition.

class PGN...