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Python Deep Learning - Second Edition
book

Python Deep Learning - Second Edition

by Ivan Vasilev, Daniel Slater, Gianmario Spacagna, Peter Roelants, Valentino Zocca
January 2019
Intermediate to advanced content levelIntermediate to advanced
386 pages
11h 13m
English
Packt Publishing
Content preview from Python Deep Learning - Second Edition

Q-learning in action

In this section, we'll use Q-learning in combination with a simple neural network to control an agent in the cart-pole task. We'll use an ε-greedy policy and experience replay. This is a classic RL problem. The agent must balance a pole attached to the cart via a joint. At every step, the agent can move the cart left or right. It receives a reward of 1 every time step that the pole is balanced. If the pole deviates by more than 15 degrees from upright, the game ends:

The cart-pole task

To help us with this, we'll use OpenAI Gym (https://gym.openai.com/), which is an open source toolkit for the development and comparison ...

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Publisher Resources

ISBN: 9781789348460Supplemental Content