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Learn Unity ML-Agents ??? Fundamentals of Unity Machine Learning

You're reading from   Learn Unity ML-Agents ??? Fundamentals of Unity Machine Learning Incorporate new powerful ML algorithms such as Deep Reinforcement Learning for games

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Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781789138139
Length 204 pages
Edition 1st Edition
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Author (1):
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Micheal Lanham Micheal Lanham
Author Profile Icon Micheal Lanham
Micheal Lanham
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Exercises

"For the things we have to learn before we can do them, we learn by doing them."
– Aritstotle

Be sure to complete the following questions or exercises on your own:

  1. Extend the bandit cube maze in the last section with your own design. Make sure to keep all the cubes connected so that the agent has a clear path to the end.
  2. Think of another problem in gaming, simulation, or otherwise, where you could use RL and the Q-Learning algorithm to help an agent learn to solve this problem. This is just a thought exercise, but give yourself a huge pat on the back if you build a demo.
  3. Add new properties for the Exploration Epsilon minimum and the amount of change per decision step. Remember, these are the parameters we hard-coded in order to decrease the epsilon-greedy exploration value.
  4. Add the ability to show the Q values on the individual BanditCube objects. If...
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Learn Unity ML-Agents ??? Fundamentals of Unity Machine Learning
Published in: Jun 2018
Publisher: Packt
ISBN-13: 9781789138139
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