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- Temporal_difference_learning abstract "Temporal difference (TD) learning is a prediction method. It has been mostly used for solving the reinforcement learning problem. "TD learning is a combination of Monte Carlo ideas and dynamic programming (DP) ideas." TD resembles a Monte Carlo method because it learns by sampling the environment according to some policy. TD is related to dynamic programming techniques because it approximates its current estimate based on previously learned estimates (a process known as bootstrapping). The TD learning algorithm is related to the temporal difference model of animal learning.As a prediction method, TD learning takes into account the fact that subsequent predictions are often correlated in some sense. In standard supervised predictive learning, one learns only from actually observed values: A prediction is made, and when the observation is available, the prediction is adjusted to better match the observation. As elucidated in, the core idea of TD learning is that we adjust predictions to match other, more accurate, predictions about the future. This procedure is a form of bootstrapping, as illustrated with the following example: Suppose you wish to predict the weather for Saturday, and you have some model that predicts Saturday's weather, given the weather of each day in the week. In the standard case, you would wait until Saturday and then adjust all your models. However, when it is, for example, Friday, you should have a pretty good idea of what the weather would be on Saturday - and thus be able to change, say, Monday's model before Saturday arrives.Mathematically speaking, both in a standard and a TD approach, we would try to optimize some cost function, related to the error in our predictions of the expectation of some random variable, E[z]. However, while in the standard approach we in some sense assume E[z] = z (the actual observed value), in the TD approach we use a model. For the particular case of reinforcement learning, which is the major application of TD methods, z is the total return and E[z] is given by the Bellman equation of the return.".
- Temporal_difference_learning wikiPageExternalLink learn_meta_ttt.
- Temporal_difference_learning wikiPageExternalLink tdgravity.
- Temporal_difference_learning wikiPageExternalLink index.html.
- Temporal_difference_learning wikiPageExternalLink Temporal_Difference_Learning.
- Temporal_difference_learning wikiPageExternalLink node2.html.
- Temporal_difference_learning wikiPageExternalLink 2000100.pdf.
- Temporal_difference_learning wikiPageExternalLink sutton-barto-90.pdf.
- Temporal_difference_learning wikiPageExternalLink tdl.html.
- Temporal_difference_learning wikiPageExternalLink CTCN.html.
- Temporal_difference_learning wikiPageExternalLink book.html.
- Temporal_difference_learning wikiPageID "1209759".
- Temporal_difference_learning wikiPageRevisionID "600355250".
- Temporal_difference_learning hasPhotoCollection Temporal_difference_learning.
- Temporal_difference_learning subject Category:Computational_neuroscience.
- Temporal_difference_learning subject Category:Machine_learning_algorithms.
- Temporal_difference_learning comment "Temporal difference (TD) learning is a prediction method. It has been mostly used for solving the reinforcement learning problem. "TD learning is a combination of Monte Carlo ideas and dynamic programming (DP) ideas." TD resembles a Monte Carlo method because it learns by sampling the environment according to some policy. TD is related to dynamic programming techniques because it approximates its current estimate based on previously learned estimates (a process known as bootstrapping).".
- Temporal_difference_learning label "Temporal difference learning".
- Temporal_difference_learning sameAs m.04hhdt.
- Temporal_difference_learning sameAs Q7698910.
- Temporal_difference_learning sameAs Q7698910.
- Temporal_difference_learning wasDerivedFrom Temporal_difference_learning?oldid=600355250.
- Temporal_difference_learning isPrimaryTopicOf Temporal_difference_learning.