Temporal difference learning

What is temporal difference learning?

Temporal Difference Learning (TD Learning) is an unsupervised learning technique that is commonly used in reinforcement learning for the purpose of predicting the total reward expected over the future. They can, however, be used to predict other quantities as well.

Essentially, TD Learning focuses on predicting a variable's future value in a sequence of states. It employs a mathematical trick that allows it to replace complicated reasoning with a simple learning procedure that can be used to generate the very same results.

Temporal Difference Learning aims to predict a combination of the immediate reward and its own reward prediction at the next moment in time. 

In TD Learning, the training signal for a prediction is a future prediction. This method is a combination of the Monte Carlo (MC) method and the Dynamic Programming (DP) method.


What are the parameters used in temporal difference learning?

  • Alpha (α): learning rate
    It shows how much our estimates should be adjusted, based on the error. This rate varies between 0 and 1.
  • Gamma (γ): the discount rate
    This indicates how much future rewards are valued. A larger discount rate signifies that future rewards are valued to a greater extent. The discount rate also varies between 0 and 1.
  • e: the ratio reflective of exploration vs. exploitation.
    This involves exploring new options with probability e and staying at the current max with probability 1-e. A larger e signifies that more exploration is carried out during training

What are the benefits of temporal difference learning?

The advantages of temporal difference learning are:

  • TD methods are able to learn in each step, online or offline.
  • These methods are capable of learning from incomplete sequences, which means that they can also be used in continuous problems.
  • Temporal difference learning can function in non-terminating environments.
  • TD Learning has less variance than the Monte Carlo method, because it depends on one random action, transition, reward.
  • It tends to be more efficient than the Monte Carlo method.
  • Temporal Difference Learning exploits the Markov property, which makes it more effective in Markov environments.


What are the disadvantages of temporal difference learning?

Temporal Difference Learning has two main disadvantages. They are:

  • Temporal Difference Learning has greater sensitivity towards the initial value.
  • It is a biased estimation.

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Temporal difference learning

October 14, 2020

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What is temporal difference learning?

Temporal Difference Learning (TD Learning) is an unsupervised learning technique that is commonly used in reinforcement learning for the purpose of predicting the total reward expected over the future. They can, however, be used to predict other quantities as well.

Essentially, TD Learning focuses on predicting a variable's future value in a sequence of states. It employs a mathematical trick that allows it to replace complicated reasoning with a simple learning procedure that can be used to generate the very same results.

Temporal Difference Learning aims to predict a combination of the immediate reward and its own reward prediction at the next moment in time. 

In TD Learning, the training signal for a prediction is a future prediction. This method is a combination of the Monte Carlo (MC) method and the Dynamic Programming (DP) method.


What are the parameters used in temporal difference learning?

  • Alpha (α): learning rate
    It shows how much our estimates should be adjusted, based on the error. This rate varies between 0 and 1.
  • Gamma (γ): the discount rate
    This indicates how much future rewards are valued. A larger discount rate signifies that future rewards are valued to a greater extent. The discount rate also varies between 0 and 1.
  • e: the ratio reflective of exploration vs. exploitation.
    This involves exploring new options with probability e and staying at the current max with probability 1-e. A larger e signifies that more exploration is carried out during training

What are the benefits of temporal difference learning?

The advantages of temporal difference learning are:

  • TD methods are able to learn in each step, online or offline.
  • These methods are capable of learning from incomplete sequences, which means that they can also be used in continuous problems.
  • Temporal difference learning can function in non-terminating environments.
  • TD Learning has less variance than the Monte Carlo method, because it depends on one random action, transition, reward.
  • It tends to be more efficient than the Monte Carlo method.
  • Temporal Difference Learning exploits the Markov property, which makes it more effective in Markov environments.


What are the disadvantages of temporal difference learning?

Temporal Difference Learning has two main disadvantages. They are:

  • Temporal Difference Learning has greater sensitivity towards the initial value.
  • It is a biased estimation.

Thanks for reading! We hope you found this helpful.

Ready to level-up your business? Click here

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