Reinforcement Learning Specialization offered by University of Alberta and Alberta Machine Inteligence Institute

The main goals of the course are:

• Understand Exploration-Exploitation tradeoff using the multi-armed bandits
• Understand the structure and components of a (finite) Markov Decision Process
• Understand the definition of the state-value function and the action-value function
• Be able to explain how to derive the Bellman equations and the Bellman optimality equations
• Understand the framework of Dynamic Programming(Policy evaluation, policy iteration and generalized policy iteration)

The main goals of the course are:

• Understand prediction problems using Monte Carlo methods
• Understand how Temporal Difference learning works in prediction problems compared to the Monte Carlo method
• Understand different TD learning methods for control problems; Q-learning, SARSA, and Expected SARSA
• Understand the Dyna architecture (Dyna-Q and Dyna-Q+)

The main goals of the course are:

• Understand how value functions are approximated using parametrized functions
• Understand what coarse coding for feature generalization is and how to use neural network for function approximation
• Be able to implement Episodic SARSA
• Understand what the average reward
• Understand how Policy gradient and the actor-critic method work for continuing tasks

The final course aims to implement a complete RL system by

• Interpretting the setting into a RL framework and identifying a proper method
• Coding our environment
• Coding our agent

Our setting is that we want a lunar lander to land on the surface of the moon without crushing.