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#109 Use network's first layer's shape to create input Vol #110

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#109 Use network's first layer's shape to create input Vol #110

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21 changes: 13 additions & 8 deletions build/deepqlearn.js
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Original file line number Diff line number Diff line change
Expand Up @@ -136,18 +136,23 @@ var deepqlearn = deepqlearn || { REVISION: 'ALPHA' };
}
}
},
policy: function(s) {
// compute the value of doing any action in this state
// and return the argmax action and its value
var svol = new convnetjs.Vol(1, 1, this.net_inputs);
policy: function (s) {
const inputLayer = this.value_net.layers[0];
const svol_sx = inputLayer.out_sx;
const svol_sy = inputLayer.out_sy;
const svol_depth = inputLayer.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 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]; }
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};
return { action: maxk, value: maxval };
},
getNetInput: function(xt) {
// return s = (x,a,x,a,x,a,xt) state vector.
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