Monte Carlo Reinforcement Learning for Simple Games
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Discover Monte Carlo methods for reinforcement learning and apply them to simple games. Learn how to train agents to make optimal decisions through simulation and feedback-based learning.
At a Glance
Have you ever thought about training your own recommendation system or building your own robot or creating your own chess AI that can beat even the most experienced player? Reinforcement Learning is what you need. In this project, you will explore the basics of Reinforcement Learning and Monte Carlo Method. You will learn about training your own agent to navigate and succeed in simple and complex games/environments. Discover better ways to train your agent and how to work with the environment.
Why you should do this Guided Project
For example, the agent will be able to guide itself through a simple environment:
A Look at the Project Ahead
- Work with an OpenAI Gym environments
- Explain what Reinforcement Learning is
- Explain what Monte Carlo Method is
- Create an agent that uses Monte Carlo Method to play Frozen Lake
- Train and Test the agents using the Frozen Lake environment
- Improve and update your algorithm.
What You’ll Need
- Knowledge of python programming language.
Frequently Asked Questions
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Do I need to install any software to participate in this project?
Everything you need to complete this project will be provided to you via the Skills Network Labs and it will all be available via a standard web browser. - What web browser should I use?
The Skills Network Labs platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari.
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