Book Image

TensorFlow 2 Reinforcement Learning Cookbook

By : Palanisamy P
Book Image

TensorFlow 2 Reinforcement Learning Cookbook

By: Palanisamy P

Overview of this book

With deep reinforcement learning, you can build intelligent agents, products, and services that can go beyond computer vision or perception to perform actions. TensorFlow 2.x is the latest major release of the most popular deep learning framework used to develop and train deep neural networks (DNNs). This book contains easy-to-follow recipes for leveraging TensorFlow 2.x to develop artificial intelligence applications. Starting with an introduction to the fundamentals of deep reinforcement learning and TensorFlow 2.x, the book covers OpenAI Gym, model-based RL, model-free RL, and how to develop basic agents. You'll discover how to implement advanced deep reinforcement learning algorithms such as actor-critic, deep deterministic policy gradients, deep-Q networks, proximal policy optimization, and deep recurrent Q-networks for training your RL agents. As you advance, you’ll explore the applications of reinforcement learning by building cryptocurrency trading agents, stock/share trading agents, and intelligent agents for automating task completion. Finally, you'll find out how to deploy deep reinforcement learning agents to the cloud and build cross-platform apps using TensorFlow 2.x. By the end of this TensorFlow book, you'll have gained a solid understanding of deep reinforcement learning algorithms and their implementations from scratch.
Table of Contents (11 chapters)

Testing/evaluating RL agents

Let’s assume that you have trained the SAC agent in one of the trading environments using the training script (previous recipe) and that you have several versions of the trained agent models, each with different policy network architectures or hyperparameters or your own tweaks and customizations to improve its performance. When you want to deploy an agent, you want to make sure that you pick the best performing agent, don’t you?

This recipe will help you build a lean script to evaluate a given pre-trained agent model locally so that you can get a quantitative performance assessment and compare several trained models before choosing the right agent model for deployment. Specifically, we will use the tradegym module and the sac_agent_runtime module that we built earlier in this chapter to evaluate the agent models that we train.

Let’s get started!

Getting ready

To complete this recipe, you will first need to activate the...