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  • License MIT

This is a simple framework for implementing and testing reinforcement learning environments and algorithms

Package Exports

  • quickrl.core
  • quickrl.core/dist/index.cjs.js
  • quickrl.core/dist/index.es.js

This package does not declare an exports field, so the exports above have been automatically detected and optimized by JSPM instead. If any package subpath is missing, it is recommended to post an issue to the original package (quickrl.core) to support the "exports" field. If that is not possible, create a JSPM override to customize the exports field for this package.

Readme

QuickRLJS-Core

Usage

QuickRLJS can be used in the frontend as well as in the backend. I recommend training in node.js and then loading and using the model in the frontend.

Example

First install the npm-package:

npm install quickrl.core

First create "training.ts" or how ever else you choose to call this file:

import { SingleAgentEnvironment, Agents, QuickRLJS } from 'quickrl.core';
// if you want to improve training performance, make shure you import this dependency
require('@tensorflow/tfjs-node-gpu');

// parameters
const randomSeed = 12;
const numTrainingEpisodes = 10000;
const logEvery = 500;
const maxIterationsPerGame = 25;

// load the environment
const env: SingleAgentEnvironment = <SingleAgentEnvironment>(
    QuickRLJS.loadEnv('BlackJack', { randomSeed: randomSeed })
);

// create an agent
const agent: Agents.DQNAgent = new Agents.DQNAgent(env, {
    learningRate: 0.0001,
    discountFactor: 0.99,
    nnLayer: [128, 128, 64],
    epsilonStart: 1,
    epsilonEnd: 0.01,
    epsilonDecaySteps: 10000,
    hiddenLayerActivation: 'relu',
    batchSize: 32,
    replayMemorySize: 10000,
    replayMemoryInitSize: 1000,
    activateDoubleDQN: true,
    updateTargetEvery: 10000,
});

// set and initialize the agent for the environment
env.agent = agent;
env.initAgent();

// train the agent
await env.train(numTrainingEpisodes, logEvery, maxIterationsPerGame);