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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In this chapter, we introduced lots of new and complex material. We became familiar with the limitations of value iteration in complex environments with large observation spaces and discussed how to overcome them with Q-learning. We checked the Q-learning algorithm on the FrozenLake environment and discussed the approximation of Q-values with neural networks and the extra complications that arise from this approximation. We covered several tricks for DQNs to improve their training stability and convergence, such as experience replay buffer, target networks, and frame stacking. Finally, we combined those extensions in to one single implementation of DQN that solves the Pong environment from the Atari games suite.

In the next chapter, we'll look at a set of tricks that researchers have found, since 2015, to improve DQN convergence and quality, which (combined) can produce state-of-the-art results on most of the 54 Atari games. This set was published in 2017 and we'll analyze and reimplement...