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Reinforcement Learning Algorithms with Python

You're reading from   Reinforcement Learning Algorithms with Python Learn, understand, and develop smart algorithms for addressing AI challenges

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Product type Paperback
Published in Oct 2019
Publisher Packt
ISBN-13 9781789131116
Length 366 pages
Edition 1st Edition
Languages
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Author (1):
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Andrea Lonza Andrea Lonza
Author Profile Icon Andrea Lonza
Andrea Lonza
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Table of Contents (19) Chapters Close

Preface 1. Section 1: Algorithms and Environments
2. The Landscape of Reinforcement Learning FREE CHAPTER 3. Implementing RL Cycle and OpenAI Gym 4. Solving Problems with Dynamic Programming 5. Section 2: Model-Free RL Algorithms
6. Q-Learning and SARSA Applications 7. Deep Q-Network 8. Learning Stochastic and PG Optimization 9. TRPO and PPO Implementation 10. DDPG and TD3 Applications 11. Section 3: Beyond Model-Free Algorithms and Improvements
12. Model-Based RL 13. Imitation Learning with the DAgger Algorithm 14. Understanding Black-Box Optimization Algorithms 15. Developing the ESBAS Algorithm 16. Practical Implementation for Resolving RL Challenges 17. Assessments
18. Other Books You May Enjoy

Introducing TensorBoard

Keeping track of how variables change during the training of a model can be a tedious job. For instance, in the linear regression example, we kept track of the MSE loss and of the parameters of the model by printing them every 40 epochs. As the complexity of the algorithms increases, there is an increase in the number of variables and metrics to be monitored. Fortunately, this is where TensorBoard comes to the rescue.

TensorBoard is a suite of visualization tools that can be used to plot metrics, visualize TensorFlow graphs, and visualize additional information. A typical TensorBoard screen is similar to the one shown in the following screenshot:

Figure 2.6: Scalar TensorBoard page

The integration of TensorBoard with TensorFlow code is pretty straightforward as it involves only a few tweaks to the code. In particular, to visualize the MSE loss over time...

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