Editing Temporal difference learning (section)

{{short description|Computer programming concept}}
{{Machine learning|Reinforcement learning}}

'''Temporal difference''' ('''TD''') '''learning''' refers to a class of [[Model-free (reinforcement learning)|model-free]] [[reinforcement learning]] methods which learn by bootstrapping from the current estimate of the value function. These methods sample from the environment, like [[Monte Carlo method]]s, and perform updates based on current estimates, like [[dynamic programming]] methods.{{sfnp|Sutton|Barto|2018|p=133}}

While Monte Carlo methods only adjust their estimates once the final outcome is known, TD methods adjust predictions to match later, more accurate, predictions about the future before the final outcome is known.<ref name="RSutton-1988">{{cite journal |last1=Sutton |first1=Richard S. |title=Learning to predict by the methods of temporal differences |journal=Machine Learning |date=1 August 1988 |volume=3 |issue=1 |pages=9–44 |doi=10.1007/BF00115009 |s2cid=207771194 |language=en |issn=1573-0565|doi-access=free }}</ref> This is a form of [[bootstrapping]], as illustrated with the following example:

<blockquote>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, Saturday's model before Saturday arrives.<ref name="RSutton-1988" /></blockquote>

Temporal difference methods are related to the temporal difference model of [[animal cognition|animal learning]].<ref name="WSchultz-1997">{{cite journal|author=Schultz, W, Dayan, P & Montague, PR.|year=1997|title=A neural substrate of prediction and reward|journal=Science|volume=275|issue=5306|pages=1593–1599|doi=10.1126/science.275.5306.1593|pmid=9054347|citeseerx=10.1.1.133.6176|s2cid=220093382 }}</ref><ref name=":0">{{Cite journal|last1=Montague|first1=P. R.|last2=Dayan|first2=P.|last3=Sejnowski|first3=T. J.|date=1996-03-01|title=A framework for mesencephalic dopamine systems based on predictive Hebbian learning|journal=The Journal of Neuroscience|volume=16|issue=5|pages=1936–1947|issn=0270-6474|pmid=8774460|pmc=6578666|doi=10.1523/JNEUROSCI.16-05-01936.1996|url=http://papers.cnl.salk.edu/PDFs/A%20Framework%20for%20Mesencephalic%20Dopamine%20Systems%20Based%20on%20Predictive%20Hebbian%20Learning%201996-2938.pdf}}</ref><ref name=":1">{{Cite journal|last1=Montague|first1=P.R.|last2=Dayan|first2=P.|last3=Nowlan|first3=S.J.|last4=Pouget|first4=A.|last5=Sejnowski|first5=T.J.|date=1993|title=Using aperiodic reinforcement for directed self-organization|url=http://www.gatsby.ucl.ac.uk/~dayan/papers/mdnps93.pdf|journal=Advances in Neural Information Processing Systems|volume=5|pages=969–976}}</ref><ref name=":2">{{Cite journal|last1=Montague|first1=P. R.|last2=Sejnowski|first2=T. J.|date=1994|title=The predictive brain: temporal coincidence and temporal order in synaptic learning mechanisms|journal=Learning & Memory|volume=1|issue=1|pages=1–33|doi=10.1101/lm.1.1.1 |issn=1072-0502|pmid=10467583|s2cid=44560099 |doi-access=free}}</ref><ref name=":3">{{Cite book|last1=Sejnowski|first1=T.J.|last2=Dayan|first2=P.|last3=Montague|first3=P.R.|title=Proceedings of the eighth annual conference on Computational learning theory - COLT '95 |chapter=Predictive Hebbian learning |date=1995|pages=15–18|doi=10.1145/225298.225300|isbn=0897917235|s2cid=1709691 |doi-access=free}}</ref>