Reinforcement Learning
Artificial Intelligence
| Beginner
- 13 videos | 26m 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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2m 52sUpon completion of this video, you will be able to describe reinforcement learning and list some of the techniques that agents can use to learn. FREE ACCESS
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2m 50sAfter completing this video, you will be able to describe additive rewards and discounted rewards. FREE ACCESS
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3. Passive Learning1m 1sAfter completing this video, you will be able to describe passive learning. FREE ACCESS
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4. Direct Utility Estimation2m 36sAfter completing this video, you will be able to describe how to use direct utility estimation for passive learning and how to define the Bellman Equation in the context of reinforced learning. FREE ACCESS
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5. Temporal Difference Learning1m 32sAfter completing this video, you will be able to describe temporal difference learning and contrast it with direct utility estimation. FREE ACCESS
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6. Active Learning2m 7sAfter completing this video, you will be able to describe active learning and contrast it with passive learning. FREE ACCESS
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7. Exploration and Exploitation Policies1m 55sAfter completing this video, you will be able to describe exploration and exploitation in the context of active reinforced learning and describe some of the exploration policies used in learning algorithms. FREE ACCESS
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8. Defining Q-learning1m 45sLearn how to define Q-learning for reinforcement learning. FREE ACCESS
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9. Implementing Q-learning2m 9sUpon completion of this video, you will be able to describe the different parts used in Q-learning and how to implement them. FREE ACCESS
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10. Off-policy and On-policy Learning1m 21sUpon completion of this video, you will be able to describe on-policy and off-policy learning and the difference between the two. FREE ACCESS
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11. Function Approximation1m 25sAfter completing this video, you will be able to 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. FREE ACCESS
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12. Deep Q-learning3m 16sUpon completion of this video, you will be able to describe how deep neural networks can approximate q-values for given states in Q-learning. FREE ACCESS
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13. Exercise: Describe Q-learning1m 28sAfter completing this video, you will be able to describe Q-learning and how to set up the algorithm for a particular problem. FREE ACCESS
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