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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Tuning hyperparameters

In the previous section, we had Pong solved in three hours of optimization and 9M frames. Now it's a good time to tweak our hyperparameters to speed up convergence. The golden rule here is to tweak one option at a time and make conclusions carefully, as the whole process is stochastic.

In this section, we'll start with the original hyperparameters and perform the following experiments:

  • Increasing the learning rate

  • Increasing the entropy beta

  • Changing the count of environments that we're using to gather experience

  • Tweaking the size of the batch

Strictly speaking, the experiments below weren't proper hyperparameter tuning, just an attempt to get a better understanding of how A2C convergence dynamics depend on the parameters. To find the best set of parameters, the full grid search or random sampling of values could give much better results, but will require much more time and resources to conduct.

Learning rate

Our starting learning rate (LR) is 0.001 and we can expect that...