Reinforcement Learning
Artificial Intelligence
13 Videos | 32m 17s - Includes Assessment
- Earns a Badge
Some problems are too complicated to describe to a computer and to solve with traditional algorithms, which is why reinforcement learning is useful. Explore the fundamentals of reinforcement learning.
WHAT YOU WILL LEARN
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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
IN THIS COURSE
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1.What Is Reinforcement Learning?2m 52sUP NEXT
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2.Additive and Discounted Rewards2m 50s
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3.Passive Learning1m 1s
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4.Direct Utility Estimation2m 36s
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5.Temporal Difference Learning1m 32s
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6.Active Learning2m 7s
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7.Exploration and Exploitation Policies1m 55s
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8.Defining Q-learning1m 45s
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9.Implementing Q-learning2m 9s
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10.Off-policy and On-policy Learning1m 21s
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11.Function Approximation1m 25s
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12.Deep Q-learning3m 16s
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13.Exercise: Describe Q-learning1m 28s
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