Some problems are too complicated to describe to a computer and to solve with traditional algorithms, which is why reinforcement learning is useful. In this course, you will learn the fundamentals of reinforcement learning.
Learning Objectives
Introducing Reinforcement Learning
- start the course
- describe reinforcement learning and list some of the techniques that agents can use to learn
- describe additive rewards and discounted rewards
- describe passive learning
- describe how to use direct utility estimation for passive learning and how to define the Bellman Equation in the context of reinforced learning
- describe temporal difference learning and contrast it with direct utility estimation
- describe active learning and contrast it with passive learning
- describe exploration and exploitation in the context of active reinforced learning and describe some of the exploration policies used in learning algorithms
Q-learning Algorithm
- define Q-learning for reinforced learning
- describe the different parts used in Q-learning and how these can be implemented
- describe on-policy and off-policy learning and the difference between the two
- describe why lookup tables aren't ideal for most reinforced learning tasks and how to build some function approximations that can make these problems possible
- describe how deep neural networks can be used to approximate q-value for given states in Q-learning
Practice: Q-learning
- describe Q-learning and how to set up the algorithm for a particular problem