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Reinforcement Learning with TensorFlow & TRFL

Video Description

Write Reinforcement Learning agents in TensorFlow & TRFL, with ease

About This Video

  • Hands-on emphasis on code examples to get you experienced with TRFL quickly.
  • Straightforward implementations of TRFL that let you utilize a trusted codebase in your projects. Save time implementing RL agents and algorithms, unit testing, and debugging code.
  • Covers the TRFL library more comprehensively than any other course. Examples teach the easy integration and expansion of RL algorithms with TRFL building blocks.

In Detail

The TRFL library is a collection of key algorithmic components that are used for a large number of DeepMind agents such as DQN, DDPG, and the Importance of Weighted Actor Learner Architecture. With this course, you will learn to implement classical RL algorithms as well as other cutting-edge techniques.

This course will help you get up-to-speed with the TRFL library quickly, so you can start building your own RL agents. Without wasting much time on theory, the course dives straightaway into designing and implementing RL algorithms.

By the end, you will be quite familiar with the tool and will be ready to put your knowledge into practice in your own projects.

The code bundle for this course is available at - https://github.com/PacktPublishing/Hands-On-Reinforcement-Learning-with-TensorFlow-TRFL

Downloading the example code for this course: You can download the example code files for all Packt video courses you have purchased from your account at http://www.PacktPub.com. If you purchased this course elsewhere, you can visit http://www.PacktPub.com/support and register to have the files e-mailed directly to you.