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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.
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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.
- Lesson 1: Markov Decision Processes
- Lesson 2: Reinforcement Learning
- Lesson 3: Game Theory
- Lesson 4: Game Theory, Continued
Use a familiar Grid world domain to train a Reinforcement Learning agent and then design an agent that can play Pac man!
Assumes 6 Hour per week (Work at your own pace)
Fees: – INR 11,940/Month (assuming $ = INR 60)
Part Time/Full time:
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- Programming experience and basic familiarity with statistics and probability theory is required.
[su_tab title = “Faculty”]
- Charles Isbell
- Michael Littman
- Pushkar Kolhe
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