# in computing errors all trainable weights are ff

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Unformatted text preview: In computing errors, all trainable weights are FF only, so we can apply the standard backpropagation algorithm The weights from the copy layer to the hidden layer play a copy hidden special role in error computation The The error signal they receive comes from the hidden units, and so depends on the error at the hidden units at time t Activations Activations in the copy units, however, are just the activation of the hidden units at time t-1 So, So, in training, we are considering a gradient of an error function which is determined by the activations at the present and the previous time steps present previous ECE 517: Reinforcement Learning in AI 14 RealReal-Time Recurrent Networks (RTRL) (Zipser et. al ’89) In deriving a gradient-based update rule, we now make gradientnetwork connectivity very unconstrained unconstrained Suppose Suppose we have a set of input units, I = {xk(t), 0<k<m}, and a set of other units, U = {yk(t), 0<k<n}, which can be hidden or output units To index an arbitrary unit in the network we can use xk (t ) if k ∈ I z k (t ) = yk (t ) if k ∈ U Let W be the weight matrix with n rows and n+m columns, n+m where wi,j is the weight to unit i (which is in U ) from unit j from (which is in I or U ) ECE 517: Reinforcement Learning in AI 15 RTRL R...
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