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train.js
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train.js
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/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
import * as fs from 'fs';
import * as argparse from 'argparse';
import {mkdir} from 'shelljs';
// The value of tf (TensorFlow.js-Node module) will be set dynamically
// depending on the value of the --gpu flag below.
let tf;
import {SnakeGameAgent} from './agent';
import {copyWeights} from './dqn';
import {SnakeGame} from './snake_game';
class MovingAverager {
constructor(bufferLength) {
this.buffer = [];
for (let i = 0; i < bufferLength; ++i) {
this.buffer.push(null);
}
}
append(x) {
this.buffer.shift();
this.buffer.push(x);
}
average() {
return this.buffer.reduce((x, prev) => x + prev) / this.buffer.length;
}
}
/**
* Train an agent to play the snake game.
*
* @param {SnakeGameAgent} agent The agent to train.
* @param {number} batchSize Batch size for training.
* @param {number} gamma Reward discount rate. Must be a number >= 0 and <= 1.
* @param {number} learnigRate
* @param {number} cumulativeRewardThreshold The threshold of moving-averaged
* cumulative reward from a single game. The training stops as soon as this
* threshold is achieved.
* @param {number} maxNumFrames Maximum number of frames to train for.
* @param {number} syncEveryFrames The frequency at which the weights are copied
* from the online DQN of the agent to the target DQN, in number of frames.
* @param {string} savePath Path to which the online DQN of the agent will be
* saved upon the completion of the training.
* @param {string} logDir Directory to which TensorBoard logs will be written
* during the training. Optional.
*/
export async function train(
agent, batchSize, gamma, learningRate, cumulativeRewardThreshold,
maxNumFrames, syncEveryFrames, savePath, logDir) {
let summaryWriter;
if (logDir != null) {
summaryWriter = tf.node.summaryFileWriter(logDir);
}
for (let i = 0; i < agent.replayBufferSize; ++i) {
agent.playStep();
}
// Moving averager: cumulative reward across 100 most recent 100 episodes.
const rewardAverager100 = new MovingAverager(100);
// Moving averager: fruits eaten across 100 most recent 100 episodes.
const eatenAverager100 = new MovingAverager(100);
const optimizer = tf.train.adam(learningRate);
let tPrev = new Date().getTime();
let frameCountPrev = agent.frameCount;
let averageReward100Best = -Infinity;
while (true) {
agent.trainOnReplayBatch(batchSize, gamma, optimizer);
const {cumulativeReward, done, fruitsEaten} = agent.playStep();
if (done) {
const t = new Date().getTime();
const framesPerSecond =
(agent.frameCount - frameCountPrev) / (t - tPrev) * 1e3;
tPrev = t;
frameCountPrev = agent.frameCount;
rewardAverager100.append(cumulativeReward);
eatenAverager100.append(fruitsEaten);
const averageReward100 = rewardAverager100.average();
const averageEaten100 = eatenAverager100.average();
console.log(
`Frame #${agent.frameCount}: ` +
`cumulativeReward100=${averageReward100.toFixed(1)}; ` +
`eaten100=${averageEaten100.toFixed(2)} ` +
`(epsilon=${agent.epsilon.toFixed(3)}) ` +
`(${framesPerSecond.toFixed(1)} frames/s)`);
if (summaryWriter != null) {
summaryWriter.scalar(
'cumulativeReward100', averageReward100, agent.frameCount);
summaryWriter.scalar('eaten100', averageEaten100, agent.frameCount);
summaryWriter.scalar('epsilon', agent.epsilon, agent.frameCount);
summaryWriter.scalar(
'framesPerSecond', framesPerSecond, agent.frameCount);
}
if (averageReward100 >= cumulativeRewardThreshold ||
agent.frameCount >= maxNumFrames) {
// TODO(cais): Save online network.
break;
}
if (averageReward100 > averageReward100Best) {
averageReward100Best = averageReward100;
if (savePath != null) {
if (!fs.existsSync(savePath)) {
mkdir('-p', savePath);
}
await agent.onlineNetwork.save(`file://${savePath}`);
console.log(`Saved DQN to ${savePath}`);
}
}
}
if (agent.frameCount % syncEveryFrames === 0) {
copyWeights(agent.targetNetwork, agent.onlineNetwork);
console.log('Sync\'ed weights from online network to target network');
}
}
}
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();
}
async function main() {
const args = parseArguments();
if (args.gpu) {
tf = require('@tensorflow/tfjs-node-gpu');
} else {
tf = require('@tensorflow/tfjs-node');
}
console.log(`args: ${JSON.stringify(args, null, 2)}`);
const game = new SnakeGame({
height: args.height,
width: args.width,
numFruits: args.numFruits,
initLen: args.initLen
});
const agent = new SnakeGameAgent(game, {
replayBufferSize: args.replayBufferSize,
epsilonInit: args.epsilonInit,
epsilonFinal: args.epsilonFinal,
epsilonDecayFrames: args.epsilonDecayFrames,
learningRate: args.learningRate
});
await train(
agent, args.batchSize, args.gamma, args.learningRate,
args.cumulativeRewardThreshold, args.maxNumFrames,
args.syncEveryFrames, args.savePath, args.logDir);
}
if (require.main === module) {
main();
}