export function parseArguments()

in snake-dqn/train.js [140:235]
96 lines of code
1 McCabe index (conditional complexity)

export function parseArguments() {
  const parser = new argparse.ArgumentParser({
    description: 'Training script for a DQN that plays the snake game'
  });
  parser.addArgument('--gpu', {
    action: 'storeTrue',
    help: 'Whether to use tfjs-node-gpu for training ' +
    '(requires CUDA GPU, drivers, and libraries).'
  });
  parser.addArgument('--height', {
    type: 'int',
    defaultValue: 9,
    help: 'Height of the game board.'
  });
  parser.addArgument('--width', {
    type: 'int',
    defaultValue: 9,
    help: 'Width of the game board.'
  });
  parser.addArgument('--numFruits', {
    type: 'int',
    defaultValue: 1,
    help: 'Number of fruits present on the board at any given time.'
  });
  parser.addArgument('--initLen', {
    type: 'int',
    defaultValue: 2,
    help: 'Initial length of the snake, in number of squares.'
  });
  parser.addArgument('--cumulativeRewardThreshold', {
    type: 'float',
    defaultValue: 100,
    help: 'Threshold for cumulative reward (its moving ' +
    'average) over the 100 latest games. Training stops as soon as this ' +
    'threshold is reached (or when --maxNumFrames is reached).'
  });
  parser.addArgument('--maxNumFrames', {
    type: 'float',
    defaultValue: 1e6,
    help: 'Maximum number of frames to run durnig the training. ' +
    'Training ends immediately when this frame count is reached.'
  });
  parser.addArgument('--replayBufferSize', {
    type: 'int',
    defaultValue: 1e4,
    help: 'Length of the replay memory buffer.'
  });
  parser.addArgument('--epsilonInit', {
    type: 'float',
    defaultValue: 0.5,
    help: 'Initial value of epsilon, used for the epsilon-greedy algorithm.'
  });
  parser.addArgument('--epsilonFinal', {
    type: 'float',
    defaultValue: 0.01,
    help: 'Final value of epsilon, used for the epsilon-greedy algorithm.'
  });
  parser.addArgument('--epsilonDecayFrames', {
    type: 'int',
    defaultValue: 1e5,
    help: 'Number of frames of game over which the value of epsilon ' +
    'decays from epsilonInit to epsilonFinal'
  });
  parser.addArgument('--batchSize', {
    type: 'int',
    defaultValue: 64,
    help: 'Batch size for DQN training.'
  });
  parser.addArgument('--gamma', {
    type: 'float',
    defaultValue: 0.99,
    help: 'Reward discount rate.'
  });
  parser.addArgument('--learningRate', {
    type: 'float',
    defaultValue: 1e-3,
    help: 'Learning rate for DQN training.'
  });
  parser.addArgument('--syncEveryFrames', {
    type: 'int',
    defaultValue: 1e3,
    help: 'Frequency at which weights are sync\'ed from the online network ' +
    'to the target network.'
  });
  parser.addArgument('--savePath', {
    type: 'string',
    defaultValue: './models/dqn',
    help: 'File path to which the online DQN will be saved after training.'
  });
  parser.addArgument('--logDir', {
    type: 'string',
    defaultValue: null,
    help: 'Path to the directory for writing TensorBoard logs in.'
  });
  return parser.parseArgs();
}