RLPark 1.0.0
Reinforcement Learning Framework in Java

LineProblem.java

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00001 package rlpark.plugin.rltoys.problems.stategraph02;
00002 
00003 import java.util.Random;
00004 
00005 import rlpark.plugin.rltoys.envio.actions.Action;
00006 import rlpark.plugin.rltoys.envio.policy.Policy;
00007 import rlpark.plugin.rltoys.envio.policy.SingleActionPolicy;
00008 
00009 public class LineProblem {
00010   static public final double Gamma = .9;
00011   public static final double Reward = 1.0;
00012   static public final State A = new State("A", 0.0);
00013   static public final State B = new State("B", 0.0);
00014   static public final State C = new State("C", 0.0);
00015   static public final State D = new State("D", Reward);
00016   static public Action Move = new Action() {
00017     private static final long serialVersionUID = -4236679466464277389L;
00018   };
00019   static public final Policy acting = new SingleActionPolicy(Move);
00020 
00021   static public GraphProblem create(Random random) {
00022     StateGraph stateGraph = new StateGraph(A, new State[] { A, B, C, D }, new Action[] { Move });
00023     stateGraph.addTransition(A, Move, B, 1.0);
00024     stateGraph.addTransition(B, Move, C, 1.0);
00025     stateGraph.addTransition(C, Move, D, 1.0);
00026     return new GraphProblem(random, A, stateGraph, new MarkovProjector(stateGraph));
00027   }
00028 }
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