The all new Reinforcement Learning Master Course is perfect blend of mathematics and coding that will aid you to kickstart RL techniques by building strong mathematical foundations. The course covers almost all areas and advanced topics in modern Reinforcement Learning starting from Markov Decision Processes, Tabular Learning Methods, Function Approximation,Q-Learning, Deep Reinforcement Learning, GamePlay and research paper discussion. We discusses several methods to train AI Agents that learn to make decisions in complex environments. We also discuss Deep Reinforcement Learning based techniques which are quite powerful and popular to solve many real life challenges using artificial intelligence. The course requirers understand of Machine Learning and Deep Learning basics as pre-requisites along with familiarity with Python and deep learning framework such as Keras/PyTorch.

Highlights

• Introduction to Reinforcement Learning
• Markov Decision Processes
• Finding optimal policies using Dynamic Programming
• Finding optimal policies using Monte Carlo Methods
• Temporal-Difference Learning
• Prediction & Control with Value Function Approximation
• Policy Gradients
• Deep Reinforcement Learning
• GamePlay & Paper Discussion
• Major Project