RLPark 1.0.0
Reinforcement Learning Framework in Java
|
00001 package rlpark.plugin.rltoys.agents.rl; 00002 00003 import rlpark.plugin.rltoys.algorithms.control.Control; 00004 import rlpark.plugin.rltoys.envio.actions.Action; 00005 import rlpark.plugin.rltoys.envio.rl.RLAgent; 00006 import rlpark.plugin.rltoys.envio.rl.TRStep; 00007 import rlpark.plugin.rltoys.math.vector.implementations.PVector; 00008 import zephyr.plugin.core.api.monitoring.annotations.Monitor; 00009 00010 public class ControlAgent implements RLAgent { 00011 private static final long serialVersionUID = 4670115173783709550L; 00012 @Monitor 00013 private final Control control; 00014 00015 public ControlAgent(Control control) { 00016 this.control = control; 00017 } 00018 00019 @Override 00020 public Action getAtp1(TRStep step) { 00021 if (step.isEpisodeEnding()) 00022 return null; 00023 return control.proposeAction(new PVector(step.o_tp1)); 00024 } 00025 }