Table of Contents
Objective Function Learning
RMSE for different approaches
- Neural Network: 0.351
- Linear Regression: 0.233
- Rule Based: 0.268
TODO:
- Reduce the feature set, e.g. remove armor and damage
- Consider additional spatial features
Neural Network
Configuration
- Input layer function: linear
- Output layer function: sigmoid
- Hidden layer function: sigmoid
- training cycles: 50000
- learning rate: 0.01
Linear Regression
Configuration
- Ridge: 1.0E-8
- Min standardized coefficent: 1.5
Parameters
- - 0.059 * numAllies
- - 0.055 * numEnemies
- + 0.981 * health
- + 0.066 * armor
- - 0.223 * basicDamage
- + 0.285 * attackPotential
- + 0.235 * distance
- - 2.179 * enemyHealth
- - 0.120 * enemyArmor
- - 0.043 * enemyBasicDamage
- - 0.238 * enemyAttackPotential
- + 0.235 * enemyDistance
- - 0.272 * healthRoot
- + 0.441 * basicDamageRoot
- - 0.111 * attackPotentialRoot
- + 0.436 * distanceRoot
- + 0.690 * enemyHealthRoot
- - 0.065 * enemyBasicDamageRoot
- + 0.155 * enemyAttackPotentialRoot
- - 0.607 * enemyDistanceRoot
- + 0.364 * healthSquare
- - 0.147 * attackPotentialSquare
- + 1.138 * distanceSquare
- + 0.940 * enemyHealthSquare
- + 0.023 * enemyBasicDamageSquare
- + 0.091 * enemyAttackPotentialSquare
- - 1.803 * enemyDistanceSquare
Rule Based
Configuration
- Player Health Factor: 1.1
- Enemy Health Factor: -1.1