Book Image

Deep Reinforcement Learning Hands-On - Second Edition

By : Maxim Lapan
Book Image

Deep Reinforcement Learning Hands-On - Second Edition

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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Let's now take a look at the results.

The feed-forward model

The convergence on Yandex data for one year requires about 10M training steps, which can take a while. (GTX 1080 Ti trains at a speed of 230-250 steps per second.)

During the training, we have several charts in TensorBoard showing us what's going on.

Figure 10.3: The reward for episodes during the training

Figure 10.4: The reward for test episodes

The two preceding charts show the reward for episodes played during the training and the reward obtained from testing (which is done on the same quotes, but with epsilon=0). From them, we see that our agent is learning how to increase the profit from its actions over time.

Figure 10.5: The lengths of played episodes

Figure 10.6: The values predicted by the network on a subset of states

The lengths of episodes also increased after 1M training iterations. The number of values predicted by the network is growing.