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Write short note on Temporal Difference Learning.
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Solution:

Temporal-Difference (TD) Learning:

  • a combination of DP and MC methods

  • updates estimates based on other learned estimates (i.e., bootstraps), (as DP methods) does not require a model; learns from raw experience as MC methods.

  • constitutes a basis for reinforcement learning.

  • Convergence to $\mathrm{V}^\pi$ is guaranteed (asymptotically as in MC methods) in the mean for a constant learning rate $\alpha$ if it is sufficiently small. with probability 1 if $\alpha$ decreases in accordance with the usual stochastic approximation conditions.

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