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| 1 | public void trainState(double[] lastObservation, double[] hiddenEnergy, double[] lastOutput, int chosenAction, double target) | |
| 2 | {
| |
| 3 | _generation++; | |
| 4 | double[] inputs = addBias(lastObservation); | |
| 5 | Tuple< double[], double[]> value = getValue(lastObservation); | |
| 6 | hiddenEnergy = value._y; | |
| 7 | lastOutput = value._x; | |
| 8 | double alpha = 0.1; | |
| 9 | double errorOutput = (1 - lastOutput[chosenAction] * lastOutput[chosenAction]) * (target - lastOutput[chosenAction]); | |
| 10 | double errorHidden; | |
| 11 | ||
| 12 | for (int j = 0; j < _hiddenSize; j++) | |
| 13 | {
| |
| 14 | errorHidden = (1 - hiddenEnergy[j] * hiddenEnergy[j]) * errorOutput * _layerWeights[1][j][chosenAction]; | |
| 15 | ||
| 16 | for (int i = 0; i < _inputSize; i++) | |
| 17 | {
| |
| 18 | updateWeight(0, errorHidden * inputs[i], errorOutput * _layerWeights[1][j][chosenAction], i, j); | |
| 19 | } | |
| 20 | } | |
| 21 | ||
| 22 | for (int i = 0; i < _hiddenSize; i++) | |
| 23 | {
| |
| 24 | updateWeight(1, errorOutput * hiddenEnergy[i], errorOutput, i, chosenAction); | |
| 25 | } | |
| 26 | } | |
| 27 | ||
| 28 | - | private void updateWeight(int layer, double gradient, double error, int hiddenNode, int outputNode) |
| 28 | + | private void updateWeight(int layer, double gradient, double error, int leftNode, int rightNode) |
| 29 | {
| |
| 30 | - | final int change = sign(gradient * _lastGradient[layer][hiddenNode][outputNode]); |
| 30 | + | final int change = sign(gradient * _lastGradient[layer][leftNode][rightNode]); |
| 31 | double weightChange = 0; | |
| 32 | - | double delta = _lastUpdateValue[layer][hiddenNode][outputNode]; |
| 32 | + | double delta = _lastUpdateValue[layer][leftNode][rightNode]; |
| 33 | ||
| 34 | if (change > 0) | |
| 35 | {
| |
| 36 | delta = Math.min(delta * POSITIVE_ETA, DELTA_MAX); | |
| 37 | weightChange = -sign(gradient) * delta; | |
| 38 | - | _lastGradient[layer][hiddenNode][outputNode] = gradient; |
| 38 | + | _lastGradient[layer][leftNode][rightNode] = gradient; |
| 39 | } else if (change < 0) | |
| 40 | {
| |
| 41 | delta = Math.max(delta * NEGATIVE_ETA, DELTA_MIN); | |
| 42 | ||
| 43 | - | if (error > _lastError[layer][hiddenNode][outputNode]) |
| 43 | + | if (error > _lastError[layer][leftNode][rightNode]) |
| 44 | {
| |
| 45 | - | weightChange = -_lastWeightChanges[layer][hiddenNode][outputNode]; |
| 45 | + | weightChange = -_lastWeightChanges[layer][leftNode][rightNode]; |
| 46 | } | |
| 47 | ||
| 48 | - | _lastGradient[layer][hiddenNode][outputNode] = 0.; |
| 48 | + | _lastGradient[layer][leftNode][rightNode] = 0.; |
| 49 | } else if (change == 0) | |
| 50 | {
| |
| 51 | weightChange = -sign(gradient) * delta; | |
| 52 | - | _lastGradient[layer][hiddenNode][outputNode] = gradient; |
| 52 | + | _lastGradient[layer][leftNode][rightNode] = gradient; |
| 53 | } | |
| 54 | ||
| 55 | - | _lastUpdateValue[layer][hiddenNode][outputNode] = delta; |
| 55 | + | _lastUpdateValue[layer][leftNode][rightNode] = delta; |
| 56 | - | _layerWeights[layer][hiddenNode][outputNode] += weightChange; |
| 56 | + | _layerWeights[layer][leftNode][rightNode] += weightChange; |
| 57 | - | _lastWeightChanges[layer][hiddenNode][outputNode] = weightChange; |
| 57 | + | _lastWeightChanges[layer][leftNode][rightNode] = weightChange; |
| 58 | - | _lastError[layer][hiddenNode][outputNode] = error; |
| 58 | + | _lastError[layer][leftNode][rightNode] = error; |
| 59 | } |
