A3CCartpole.java

package org.deeplearning4j.examples.rl4j;

import org.deeplearning4j.rl4j.space.Box;
import org.deeplearning4j.rl4j.learning.async.a3c.discrete.A3CDiscrete;
import org.deeplearning4j.rl4j.learning.async.a3c.discrete.A3CDiscreteDense;
import org.deeplearning4j.rl4j.mdp.gym.GymEnv;
import org.deeplearning4j.rl4j.network.ac.ActorCriticFactorySeparate;
import org.deeplearning4j.rl4j.network.ac.ActorCriticFactorySeparateStdDense;
import org.deeplearning4j.rl4j.util.DataManager;

/**
 * @author rubenfiszel (ruben.fiszel@epfl.ch) on 8/18/16.
 *
 * main example for A3C on cartpole
 *
 */
public class A3CCartpole {

    private static A3CDiscrete.A3CConfiguration CARTPOLE_A3C =
            new A3CDiscrete.A3CConfiguration(
                    123,            //Random seed
                    200,            //Max step By epoch
                    500000,         //Max step
                    16,              //Number of threads
                    5,              //t_max
                    10,             //num step noop warmup
                    0.01,           //reward scaling
                    0.99,           //gamma
                    10.0           //td-error clipping
            );


    private static final ActorCriticFactorySeparateStdDense.Configuration CARTPOLE_NET_A3C =  ActorCriticFactorySeparateStdDense.Configuration
    .builder().learningRate(1e-2).l2(0).numHiddenNodes(16).numLayer(3).build();

    public static void main( String[] args )
    {
        A3CcartPole();
    }

    public static void A3CcartPole() {

        //record the training data in rl4j-data in a new folder
        DataManager manager = new DataManager(true);

        //define the mdp from gym (name, render)
        GymEnv mdp = null;
        try {
        mdp = new GymEnv("CartPole-v0", false, false);
        } catch (RuntimeException e){
            System.out.print("To run this example, download and start the gym-http-api repo found at https://github.com/openai/gym-http-api.");
        }

        //define the training
        A3CDiscreteDense<Box> dql = new A3CDiscreteDense<Box>(mdp, CARTPOLE_NET_A3C, CARTPOLE_A3C, manager);

        //start the training
        dql.train();

        //close the mdp (http connection)
        mdp.close();

    }



}