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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Index

Alternative ways of exploration

In this section, we will cover an overview of a set of alternative approaches to the exploration problem. This won't be an exhaustive list of approaches that exist, but rather will provide an outline of the landscape.

We're going to check three different approaches to exploration:

  • Randomness in the policy, when stochasticity is added to the policy that we use to get samples. The method in this family is noisy networks, which we have already covered.
  • Count-based methods, which keep track of the count of times the agent has seen the particular state. We will check two methods: the direct counting of states and the pseudo-count method.
  • Prediction-based methods, which try to predict something from the state and from the quality of the prediction. We can make judgements about the familiarity of the agent with this state. To illustrate this approach, we will take a look at the policy distillation method, which has shown state-of...