RLPark 1.0.0
Reinforcement Learning Framework in Java

ConstantPolicy.java

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00001 package rlpark.plugin.rltoys.envio.policy;
00002 
00003 import java.util.Random;
00004 
00005 import rlpark.plugin.rltoys.envio.actions.Action;
00006 import rlpark.plugin.rltoys.math.vector.RealVector;
00007 
00008 public class ConstantPolicy extends StochasticPolicy {
00009   private static final long serialVersionUID = 9106677500699183729L;
00010   protected final double[] distribution;
00011 
00012   public ConstantPolicy(Random random, Action[] actions, double[] distribution) {
00013     super(random, actions);
00014     assert actions.length == distribution.length;
00015     this.distribution = distribution;
00016   }
00017 
00018   @Override
00019   public double pi(Action a) {
00020     return distribution[atoi(a)];
00021   }
00022 
00023   @Override
00024   public Action sampleAction() {
00025     return chooseAction(distribution);
00026   }
00027 
00028   @Override
00029   public double[] distribution() {
00030     return distribution;
00031   }
00032 
00033   @Override
00034   public void update(RealVector x) {
00035   }
00036 }
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