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Parameters definition in index.js
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| // type of neural network to train | |
| const NETWORK_TYPE = 'CNN'; | |
| // number of games to play | |
| const LEARN_TIMES = 10000; | |
| // learningRate is progressively decreased with the number of games until | |
| // the final value LR_INIT/LR_FINAL_FRACTION | |
| const LR_INIT = 0.0001; | |
| const LR_FINAL_FRACTION = 10; | |
| // epsilon is the ratio between exploration and exploitation | |
| // it can evolve along the games played | |
| const EPSILON_INIT = 0.1; | |
| const EPSILON_FINAL = 0.4; | |
| // gamma is the fraction attributed to the maximum Q Value of the next state | |
| const GAMMA = 0.3; | |
| // reward awarded to the final play that led to victory | |
| const REWARD = 100; | |
| // discount applied to the reward awarded to the previous plays that led to victory | |
| const DISCOUNT = 0.8; | |
| // reward (or - reward) awarded to prevent the bot to lose | |
| const SIDE_REWARD = 75; | |
| // network configuration | |
| const NETWORK_CONFIG = { | |
| CNN: { | |
| filters_number: 32, | |
| padding: 2, | |
| stride: 1, | |
| size: 4, | |
| layers : [60, 30] | |
| }, | |
| NN: { | |
| layers: [100] | |
| } | |
| } |
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