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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.
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
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
describe Q-learning and how to set up the algorithm for a particular problem
The materials within this course focus on the Knowledge Skills and Abilities (KSAs) identified within the Specialty Areas listed below. Click to view Specialty Area details within the interactive National Cybersecurity Workforce Framework.