Machine Learning: Reinforcement Learning – Udacity

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This Reinforcement Learning course will teach you the algorithms for designing self-learning agents like us!

Reinforcement Learning is the area of Machine Learning concerned with the actions that software agents ought to take in a particular environment in order to maximize rewards. You can apply Reinforcement Learning to robot control, chess, backgammon, checkers, and other activities that a software agent can learn. Reinforcement Learning uses behaviorist psychology in order to achieve reward maximization.

In this course, you will gain an understanding of topics and methods in Reinforcement Learning, including Markov Decision Processes and Game Theory. You will gain experience implementing Reinforcement Learning techniques in a final project.

In the final project, we’ll bring back the 80’s and design a Pacman agent capable of eating all the food without getting eaten by monsters.

Course Contents

  • Lesson 1: Markov Decision Processes
  • Lesson 2: Reinforcement Learning
  • Lesson 3: Game Theory
  • Lesson 4: Game Theory, Continued

Projects

Use a familiar Grid world domain to train a Reinforcement Learning agent and then design an agent that can play Pac man!

Duration:

1 month

Assumes 6 Hour per week (Work at your own pace)

Fees: – INR 11,940/Month (assuming $ = INR 60)

Part Time/Full time:

Part Time

  • Programming experience and basic familiarity with statistics and probability theory is required.
  • Charles Isbell
  • Michael Littman
  • Pushkar Kolhe
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