How to efficiently update probabilities within an EnumeratedDistribution instance?

Question Summary

Is there any way of updating the probabilities within an existing instance of the class EnumeratedIntegerDistribution without creating an entirely new instance?

Background

I'm trying to implement a simplified Q-learning style demonstration using an android phone. I need to update the probabilities for each item with each loop through the learning process. Currently I am unable to find any method accessible from my instance of enumeratedIntegerDistribution that will let me reset|update|modify these probabilities. Therefore, the only way I can see to do this is to create a new instance of EnumeratedIntegerDistribution within each loop. Keeping in mind that each of these loops is only 20ms long, it is my understanding that this would be terribly memory inefficient compared to creating one instance and updating the values within the existing instance. Is there no standard set-style methods to update these probabilities? If not, is there a recommended workaround (i.e. using a different class, making my own class, overriding something to make it accessible, etc.?)

A follow up would be whether or not this question is a moot effort. Would the compiled code actually be any more/less efficient by trying to avoid this new instance every loop? (I'm not knowledgeable enough to know how compilers would handle such things).

Code

A minimal example below:

package com.example.mypackage.learning;  

import android.app.Activity;  
import android.os.Bundle;  
import org.apache.commons.math3.distribution.EnumeratedIntegerDistribution;  


public class Qlearning extends Activity {  

    private int selectedAction;  
    private int[] actions = {0, 1, 2};  
    private double[] weights = {1.0, 1.0, 1.0};  
    private double[] qValues = {1.0, 1.0, 1.0};  
    private double qValuesSum;  
    EnumeratedIntegerDistribution enumeratedIntegerDistribution = new EnumeratedIntegerDistribution(actions, weights);  
    private final double alpha = 0.001;  
    int action;  
    double reward;  

    @Override  
    protected void onCreate(Bundle savedInstanceState) {  
        super.onCreate(savedInstanceState);  
        while(true){  
            action = determineAction();  
            reward = determineReward();  
            learn(action, reward);  
        }  
    }  

    public void learn(int action, double reward) {  
        qValues[selectedAction] = (alpha * reward) + ((1.0 - alpha) * qValues[selectedAction]);  
        qValuesSum = 0;  
        for (int i = 0; i < qValues.length; i++){  
            qValuesSum += Math.exp(qValues[i]);  
        }  
        weights[selectedAction] = Math.exp(qValues[selectedAction]) / qValuesSum;  
        // *** This seems inefficient ***  
        EnumeratedIntegerDistribution enumeratedIntegerDistribution = new EnumeratedIntegerDistribution(actions, weights);  
    }  
}

Please don't focus on the absence of the determineAction() or determineReward() methods, as this is simply a minimal example. You could easily just sub in fixed values there (e.g. 1, and 1.5) if you wanted a working example.

Also, I'm well aware of the infinite while loop that would be troublesome for a GUI, but again, just trying to reduce the code I have to show here to get the point across.

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