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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.mdp.core.action.Action;
import burlap.mdp.core.state.State;


/**
 * An interface for defining a {@link Policy}. Requires providing an action mapping (or sampling in the case
 * of stochastic policies) specifying the policy distribution, indicating whether the policy is stochastic,
 * and checking if the policy is defined for an input state.
 * 
 * Various helper methods, including methods to rollout a policy from a model or in an environment are
 * included in the {@link PolicyUtils} class.
 *
 *
 * @author James MacGlashan
 *
 */
public interface Policy {


	
	/**
	 * This method will return an action sampled by the policy for the given state. If the defined policy is
	 * stochastic, then multiple calls to this method for the same state may return different actions. The sampling
	 * should be with respect to defined action distribution that is returned by getActionDistributionForState
	 * @param s the state for which an action should be returned
	 * @return a sample action from the action distribution; null if the policy is undefined for s
	 */
	Action action(State s);

	/**
	 * Returns the probability/probability density that the given action will be taken in the given state.
	 * @param s the state of interest
	 * @param a the action that may be taken in the state
	 * @return the probability/probability density
	 */
	double actionProb(State s, Action a);

	/**
	 * Specifies whether this policy is defined for the input state.
	 * @param s the input state to test for whether this policy is defined
	 * @return true if this policy is defined for {@link State} s, false otherwise.
	 */
	boolean definedFor(State s);


}