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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Noisy networks

The next improvement that we're going to check addresses another RL problem: exploration of the environment. The paper is called Noisy Networks for Exploration ([4] Fortunato and others, 2017) and has a very simple idea for learning exploration characteristics during training, instead of having a separate schedule related to the exploration.

Classical DQN achieves exploration by choosing random actions with specially defined hyperparameter epsilon, which is slowly decreased over time from 1.0 (fully random actions) to some small ratio of 0.1 or 0.02. This process works well for simple environments with short episodes, without much non-stationarity during the game, but even in such simple cases, it requires tuning to make training processes efficient.

In the above-mentioned paper, the authors propose a quite simple solution, which, nevertheless, works well. They add a noise to the weights of fully-connected layers of the network and adjust the parameters of this noise during...