| Description |
1 online resource (streaming video file) (3 hours, 42 minutes, 6 seconds) |
| Summary |
Train your agent using Reinforcement Learning with Tensorflow's neural networks, OpenAI Gym and Python, to make it smarter About This Video Practical training in the Reinforcement Learning architecture for training agents Work with important open source Reinforcement Learning frameworks to get an in-depth knowledge of its functions A Production-ready approach to training Reinforcement Learning agents in Tensorflow to take on real-world projects In Detail You've probably heard of Deepmind's AI playing games and getting really good at playing them (like AlphaGo beating the Go world champion). Such agents are built with the help of a paradigm of machine learning called “Reinforcement Learning” (RL). In this course, you'll walk through different approaches to RL. You'll move from a simple Q-learning to a more complex, deep RL architecture and implement your algorithms using Tensorflow's Python API. You'll be training your agents on two different games in a number of complex scenarios to make them more intelligent and perceptive. By the end of this course, you'll be able to implement RL-based solutions in your projects from scratch using Tensorflow and Python. The code bundle for this video course is available at: https://github.com/PacktPublishing/-Hands-on-Reinforcement-Learning-with-TensorFlow |
| Notes |
Mode of access: World Wide Web |
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Packt Publishing 2018 |
| Issuing Body |
Made available through: Safari, an O'Reilly Media Company |
| Subject |
Python (Computer program language)
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| Form |
Streaming video
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| Author |
Safari, an O'Reilly Media Company
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