Construct · 1 fields · 1 citations

least-squares temporal difference: a standalone construct

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Reinforcement learning

a policy evaluation method that solves for linear value function weights directly by least squares instead of incremental updates.

In reinforcement learning, least-squares temporal difference refers to a policy evaluation method that solves for linear value function weights directly by least squares instead of incremental updates. This meaning comes from Bradtke (1996) in Machine Learning.[1]

Unit
Recovery means
Measured by

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Nearby constructs

function5 fields · 6 citationsjingle · one word, five constructs learning6 fields · 14 citationsjingle · one name, six constructs policy2 fields · 4 citationsjingle · one word, two constructs

References

Bradtke, S. J., & Barto, A. G. (1996). Linear Least-Squares algorithms for temporal difference learning. Machine Learning, 22(1-3), 33-57. https://doi.org/10.1007/bf00114723

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