RLPark 1.0.0
Reinforcement Learning Framework in Java
|
00001 package rlpark.plugin.rltoys.algorithms.control.acting; 00002 00003 import rlpark.plugin.rltoys.envio.actions.Action; 00004 import rlpark.plugin.rltoys.envio.policy.Policies; 00005 import rlpark.plugin.rltoys.envio.policy.Policy; 00006 import rlpark.plugin.rltoys.math.vector.RealVector; 00007 00008 public class ControlPolicyAdapter implements PolicyBasedControl { 00009 private static final long serialVersionUID = 7405967970830537947L; 00010 private final Policy policy; 00011 00012 public ControlPolicyAdapter(Policy policy) { 00013 this.policy = policy; 00014 } 00015 00016 @Override 00017 public Action step(RealVector x_t, Action a_t, RealVector x_tp1, double r_tp1) { 00018 return Policies.decide(policy, x_tp1); 00019 } 00020 00021 @Override 00022 public Action proposeAction(RealVector x) { 00023 return Policies.decide(policy, x); 00024 } 00025 00026 @Override 00027 public Policy policy() { 00028 return policy; 00029 } 00030 }