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In this paper, we present a \lambda -schedule procedure that generalizes the TD (\lambda) algorithm to the case when the parameter \lambda could vary with time ...
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Using this procedure, we develop an on-policy algorithm called TD(λ)- schedule and two off-policy algorithms called GTD(λ)-schedule and TDC(λ)-schedule. We ...
Nov 23, 2021 · IEEE Transactions on Automatic Control, 42(5):674–. 690, 1997. Page 10. Schedule Based Temporal Difference Algorithms. Appendix. A1.
Sep 27, 2022 · In this paper, we present a <tex>$\lambda$</tex> -schedule procedure that generalizes the TD <tex>$(\lambda)$</tex> algorithm to the case when ...
This paper proposes an on-policy algorithm - TD $(\lambda)\text{-}$ schedule, and two off-policy algorithms - GTD $(\lambda)$ -schedule and TDC -sche ...
Temporal Difference (TD) learning algorithms are a popular class of algorithms for solving the prediction problem. TD algorithms with linear function ...
Apr 20, 2024 · This paper studies the flexible double shop scheduling problem (FDSSP) that considers simultaneously job shop and assembly shop.
Temporal difference (TD) learning refers to a class of model-free reinforcement learning methods which learn by bootstrapping from the current estimate of the ...
Mar 27, 2018 · The main problem with TD learning and DP is that their step updates are biased on the initial conditions of the learning parameters.
Dec 9, 2015 · Temporal Difference is an approach to learning how to predict a quantity that depends on future values of a given signal.