Reinforcement Learning with TensorFlow & TRFL [Video]
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-TRFLStyle and Approach
In each section of the course, we walk through part of the TRFL library. We explain how TRFL is used with clear code examples that highlight integrating TRFL into TensorFlow code, making it easy to deploy TRFL in new or existing projects. While this course emphasizes practical TRFL usage, we provide explanations that relate the TRFL library to the underlying theory and provide further resources for those wanting to know more.
|Course Length||1 hour 19 minutes|
|Date Of Publication||18 Apr 2019|
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