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

  • 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

  • 2m 52s
    Upon 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
  • 2m 50s
    After completing this video, you will be able to describe additive rewards and discounted rewards. FREE ACCESS
  • Locked
    3.  Passive Learning
    1m 1s
    After completing this video, you will be able to describe passive learning. FREE ACCESS
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    4.  Direct Utility Estimation
    2m 36s
    After 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
  • Locked
    5.  Temporal Difference Learning
    1m 32s
    After completing this video, you will be able to describe temporal difference learning and contrast it with direct utility estimation. FREE ACCESS
  • Locked
    6.  Active Learning
    2m 7s
    After completing this video, you will be able to describe active learning and contrast it with passive learning. FREE ACCESS
  • Locked
    7.  Exploration and Exploitation Policies
    1m 55s
    After 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-learning
    1m 45s
    Learn how to define Q-learning for reinforcement learning. FREE ACCESS
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    9.  Implementing Q-learning
    2m 9s
    Upon 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 Learning
    1m 21s
    Upon 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 Approximation
    1m 25s
    After 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-learning
    3m 16s
    Upon 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-learning
    1m 28s
    After 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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