RLPark 1.0.0
Reinforcement Learning Framework in Java

PolicyDistributionAdapter.java

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00001 package rlpark.plugin.rltoys.algorithms.functions.policydistributions.helpers;
00002 
00003 import rlpark.plugin.rltoys.algorithms.functions.policydistributions.PolicyDistribution;
00004 import rlpark.plugin.rltoys.envio.actions.Action;
00005 import rlpark.plugin.rltoys.envio.policy.Policy;
00006 import rlpark.plugin.rltoys.math.vector.RealVector;
00007 import rlpark.plugin.rltoys.math.vector.implementations.PVector;
00008 
00009 public class PolicyDistributionAdapter implements PolicyDistribution {
00010   private static final long serialVersionUID = -3702175603455756729L;
00011   private final Policy policy;
00012 
00013   public PolicyDistributionAdapter(Policy policy) {
00014     this.policy = policy;
00015   }
00016 
00017   @Override
00018   public double pi(Action a) {
00019     return policy.pi(a);
00020   }
00021 
00022   @Override
00023   public Action sampleAction() {
00024     return policy.sampleAction();
00025   }
00026 
00027   @Override
00028   public PVector[] createParameters(int nbFeatures) {
00029     return new PVector[] {};
00030   }
00031 
00032   @Override
00033   public RealVector[] computeGradLog(Action a_t) {
00034     return new PVector[] {};
00035   }
00036 
00037   @Override
00038   public int nbParameterVectors() {
00039     return 0;
00040   }
00041 
00042   @Override
00043   public void update(RealVector x) {
00044     policy.update(x);
00045   }
00046 }
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