Monte Carlo Reinforcement Learning for Simple Games
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Discover the power of Reinforcement Learning and Monte Carlo Method in training your own recommendation system, building robots, or creating chess AI. Learn how to navigate and succeed in games/environments with your own agent. Explore the world of Reinforcement Learning with the help of open AI’s gym framework.
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.
Deep Blue a chess computer beat world chess champion Garry Kasparov in 1997. The game of Go is played on a 19 by 19 board and is much more difficult to play as there are about 10 to the power 360 different combinations. It was thought it would take decades before a computer beat a Go champion. But now, thanks to reinforcement learning, computers can easily beat Go champions, beat Chess Grandmasters and outperform Humans in every game. In this project, you will use Monte Carlo Reinforcement learning algorithms for the simple game Frozen lake. You will quickly grape import concepts of Reinforcement learning and apply open AI’s gym, the go-to framework for Reinforcement learning.
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