/* * i_Learner_id.java */package EDU.gatech.cc.is.clay;import java.lang.*;import EDU.gatech.cc.is.learning.*;/** * A node that uses a reinforcement learning module to learn  * over time which output to select, given the current state  * and reward. * <P> * For detailed information on how to configure behaviors, see the * <A HREF="../clay/docs/index.html">Clay page</A>. * <P> * <A HREF="../COPYRIGHT.html">Copyright</A> * (c)1997, 1998 Tucker Balch * * @author Tucker Balch * @version $Revision: 1.1 $ */public class i_Learner_id extends NodeInt	{	private	i_ReinforcementLearner_id rl;	private	int	lastval=0;	private	int	laststate=-1;	private int	firsttime=1;	private long	lasttime=-1;	/**	 * Instantiate a reinforcement learning node with a specified 	 * learning module.	 *	 * @param m  i_ReinforcementLearning_id, the learning module.	 * @param e1 NodeInt, a node that provides state information.	 * @param e2 NodeScalar, a node that provides the reinforcement	 *                       signal.	 */	public i_Learner_id(i_ReinforcementLearner_id m, NodeInt e1,		NodeScalar e2)		{		rl = m;		embedded_nodes = new Node[2];		embedded_nodes[0] = e1;		embedded_nodes[1] = e2;		}		/**	 * Get the value of the node.	 * 	 * @param timestamp long, the time of the request.	 */	public int Value(long timestamp)		{		if ((timestamp != lasttime)||(timestamp == -1))			{			if (timestamp != -1) lasttime = timestamp;			/*--- get state and reward values ---*/			int state = ((NodeInt)embedded_nodes[0]).Value(timestamp);			double reward = ((NodeScalar)embedded_nodes[1]).doubleValue(				timestamp);			/*--- call initTrial if it is the first call ---*/			if (firsttime==1)				{				lastval = rl.initTrial(state);				firsttime = 0;				}			/*--- call query otherwise ---*/			else				{				/* Only query if the state has changed */				if (state != laststate)					lastval = rl.query(state,reward);				}			laststate = state;			}		return lastval;		}	}