Sentence examples for update of weights from inspiring English sources

In reinforcement learning, the difference between actual and expected rewards plays an important role for the update of weights in Q-learning, SARSA, and related variants of temporal difference learning (Sutton and Barto 1998).

This assumption leads to a recursive update of the weights as (2).

A gradient based update of the weights is performed at discrete time instants over a moving measurement window in order to reduce the model output – real output mismatch.

If we sample from the Markov kernels of X [ t 0, ∞ ) directly, then ϱ τ d ∗ | τ d - 1 x τ d ∗ i | x τ d - 1 i ≡ 1 and the update of the weights does not depend on the new states x τ d ∗ i. Hence we only need to compute the weight update and the corresponding ESS estimate until we find an adequate stepsize.

Note that if one chooses X ~ [ t 0, ∞ ) = X [ t 0, ∞ ) (in law), then ϱ t k | t k - 1 (x t k i | x t k - 1 i ) ≡ 1 and the update of the weights simplifies to w t k i = g k y k | x t k i, t k w t k - 1 i.

This block update of tap weight vector greatly improves computational complexity and convergence rate.

Each iteration sees an update of the weight by the computed gradient.

To investigate the performance of the adaptive updating of the weights for this algorithm, we have used the optimal weights as initial weights.

After trial t is completed, the 'ground truth' value y t becomes known and is used to compute the estimation error based on a loss function, L. The estimation error computed at trial t for every expert i is thus given by Li,t(x i, y t ) which is used to update a set of weights.

Line 7 updates the unnormalized UAV position and line 8 updates the sum of weights.

When early stopping is used, 20% of the data will be selected by stratified random sampling to constitute a validation set, which is left outside of the updating of the weights.