Reinforcement Learning

Artificial Intelligence
  • 13 Videos | 32m 17s
  • Includes Assessment
  • Earns a Badge
Likes 27 Likes 27
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

  • Playable
    1. 
    What Is Reinforcement Learning?
    2m 52s
    UP NEXT
  • Playable
    2. 
    Additive and Discounted Rewards
    2m 50s
  • Locked
    3. 
    Passive Learning
    1m 1s
  • Locked
    4. 
    Direct Utility Estimation
    2m 36s
  • Locked
    5. 
    Temporal Difference Learning
    1m 32s
  • Locked
    6. 
    Active Learning
    2m 7s
  • Locked
    7. 
    Exploration and Exploitation Policies
    1m 55s
  • Locked
    8. 
    Defining Q-learning
    1m 45s
  • Locked
    9. 
    Implementing Q-learning
    2m 9s
  • Locked
    10. 
    Off-policy and On-policy Learning
    1m 21s
  • Locked
    11. 
    Function Approximation
    1m 25s
  • Locked
    12. 
    Deep Q-learning
    3m 16s
  • Locked
    13. 
    Exercise: Describe Q-learning
    1m 28s

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