Temporal Difference Updating without a Learning Rate

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dc.creatorHutter, Marcus
dc.creatorLegg, Shane
dc.date2008-10-31
dc.date.accessioned2026-07-07T10:14:33Z
dc.date.available2026-07-07T10:14:33Z
dc.descriptionWe derive an equation for temporal difference learning from statistical principles. Specifically, we start with the variational principle and then bootstrap to produce an updating rule for discounted state value estimates. The resulting equation is similar to the standard equation for temporal difference learning with eligibility traces, so called TD(lambda), however it lacks the parameter alpha that specifies the learning rate. In the place of this free parameter there is now an equation for the learning rate that is specific to each state transition. We experimentally test this new learning rule against TD(lambda) and find that it offers superior performance in various settings. Finally, we make some preliminary investigations into how to extend our new temporal difference algorithm to reinforcement learning. To do this we combine our update equation with both Watkins' Q(lambda) and Sarsa(lambda) and find that it again offers superior performance without a learning rate parameter.
dc.description12 pages, 6 figures
dc.identifierhttps://arxiv.org/abs/0810.5631
dc.identifierhttp://arxiv.org/abs/0810.5631
dc.identifierAdvances in Neural Information Processing Systems 20 (NIPS 2008) pages 705-712
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/172915
dc.subjectMachine Learning
dc.subjectArtificial Intelligence
dc.titleTemporal Difference Updating without a Learning Rate
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