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There is a problem with this line:
convnetjs/build/deepqlearn.js
Line 142 in 4c3358a
the shape of the input vector is basically hardcoded here when given to the network's forward function. Which makes it all break apart in line
convnetjs/src/convnet_layers_dotproducts.js
Line 67 in 4c3358a
The text was updated successfully, but these errors were encountered:
My current workaround is to do this:
deepqlearn.Brain.prototype.policy = function(s) { const svol_sx = this.value_net.layers[0].out_sx; const svol_sy = this.value_net.layers[0].out_sy; const svol_depth = this.value_net.layers[0].out_depth; var svol = new convnetjs.Vol(svol_sx, svol_sy, svol_depth); svol.w = s; var action_values = this.value_net.forward(svol); var maxk = 0; var maxval = action_values.w[0]; for(var k=1;k<this.num_actions;k++) { if(action_values.w[k] > maxval) { maxk = k; maxval = action_values.w[k]; } } return {action:maxk, value:maxval}; };```
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There is a problem with this line:
convnetjs/build/deepqlearn.js
Line 142 in 4c3358a
the shape of the input vector is basically hardcoded here when given to the network's forward function. Which makes it all break apart in line
convnetjs/src/convnet_layers_dotproducts.js
Line 67 in 4c3358a
Where we try to check for existence of those dimensions.
The text was updated successfully, but these errors were encountered: