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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.behavior.policy;

import burlap.behavior.policy.support.ActionProb;
import burlap.mdp.core.state.State;

import java.util.List;

/**
 * An interface extension to {@link Policy} for policies that can enumerate their probability distribution over all actions.
 * @author James MacGlashan.
 */
public interface EnumerablePolicy extends Policy {

	/**
	 * This method will return action probability distribution defined by the policy. The action distribution is represented
	 * by a list of ActionProb objects, each which specifies a grounded action and a probability of that grounded action being
	 * taken. The returned list does not have to include actions with probability 0.
	 * @param s the state for which an action distribution should be returned
	 * @return a list of possible actions taken by the policy and their probability.
	 */
	List policyDistribution(State s);


}