AI Reinforcement Learning

Colleagues, the AI “Reinforcement Learning” program  introduces you to Reinforcement Learning, an area of Machine Learning. You will learn the Markov Decision Processes, Bandit Algorithms, Dynamic Programming, and Temporal Difference (TD) methods. You will be introduced to Value function, Bellman Equation, and Value iteration. You will also learn Policy Gradient methods. You will learn to make decisions in an uncertain environment. Skill-based training modules include: 1) Introduction to Reinforcement Learning the fundamentals of RL and its elements. This module also introduces you to OpenAI Gym - a programming environment used for implementing RL agents, Branches of Machine Learning, What is Reinforcement Learning?, Reinforcement Learning Process, Elements of Reinforcement Learning, RL Agent Taxonomy, Reinforcement Learning Problem, Introduction to OpenAI Gym; 2) Bandit Algorithms and Markov Decision Process - Bandit Algorithms, Markov Process, Reward Process & Decision Process; 3) Dynamic Programming & Temporal Difference Methods - Introduction to Dynamic Programming, Dynamic Programming Algorithms, Monte Carlo Methods, Temporal Difference Learning Methods; and 4) Deep Q Learning - Policy Gradients, Gradients using TensorFlow, Deep Q learning and Q learning with replay buffers, target networks, and CNN along with an in-class project to provide you hands-on experience in Reinforcement Learning.

Enroll today (teams & executives are welcome): https://tinyurl.com/2kdsm5xh 

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