Build your first game using Reinforcement Learning

We have been hearing news of artificial intelligence systems outperforming humans in many tasks, some of the noticeable victories by AI are AlphaGo, DOTA2, StarCraft II etc. Reinforcement learning in a domain in machine learning requires the agent in a potentially complex environment to learn and achieve a goal under uncertainty. Reinforcement learning is applied to various fields like robotics, pattern recognition, personalized medical treatment, drug discovery, speech recognition and many more. In this course, you will learn about reinforcement learning and deep reinforcement learning, building games, and other essential algorithms in reinforcement learning. If you are a beginner-level data scientist or interested in learning advanced ML/AI topics, in that case, this course will provide you with an in-depth understanding of reinforcement learning theory and programming techniques.

Outline

1. Introduction to Reinforcement Learning
2. Introduction to Deep Reinforcement Learning
3. Hands-on Reinforcement Learning Frameworks
4. Introduction to Policy-Based Methods
5. Implement Deep Q-network with PyTorch to play a game
6. Hands-on Open Source toolkits for Deep Reinforcement Learning
7. Recent Advances and Applications

Note: ADaSci Members get 50% discount and Premium Members get 100% discount on the course.