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The Brown-UMBC Reinforcement Learning and Planning (BURLAP) Java code library is for the use and development of single or multi-agent planning and learning algorithms and domains to accompany them. The library uses a highly flexible state/observation representation where you define states with your own Java classes, enabling support for domains that discrete, continuous, relational, or anything else. Planning and learning algorithms range from classic forward search planning to value-function-based stochastic planning and learning algorithms.

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package burlap.mdp.stochasticgames.common;

import burlap.mdp.core.state.State;
import burlap.mdp.stochasticgames.JointAction;
import burlap.mdp.stochasticgames.model.JointRewardFunction;

/**
 * A Joint reward function that always returns zero reward for each agent.
 * @author James MacGlashan.
 */
public class NullJointRewardFunction implements JointRewardFunction {

	@Override
	public double[] reward(State s, JointAction ja, State sp) {
		double [] r = new double[ja.size()];
		return r;
	}
}