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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.stochasticgames.agents.madp;

import burlap.behavior.stochasticgames.PolicyFromJointPolicy;
import burlap.behavior.stochasticgames.madynamicprogramming.MADynamicProgramming;
import burlap.mdp.stochasticgames.agent.SGAgent;
import burlap.mdp.stochasticgames.agent.AgentFactory;
import burlap.mdp.stochasticgames.SGDomain;
import burlap.mdp.stochasticgames.agent.SGAgentType;


/**
 * An agent factory for the {@link MultiAgentDPPlanningAgent} agent. Generated agents are always provided a copy of the provided
 * policy object to ensure that multiple agents from the same factory use a policy specific to them.
 * @author James MacGlashan
 *
 */
public class MADPPlanAgentFactory implements AgentFactory {

	protected SGDomain						domain;
	protected MADPPlannerFactory plannerFactory;
	protected PolicyFromJointPolicy			policy;
	
	
	/**
	 * Initializes.
	 * @param domain the domain for the agents
	 * @param planner the valueFunction object that will be used by all generated agents
	 * @param policy the policy that will be copied and supplied to all generated objects
	 */
	public MADPPlanAgentFactory(SGDomain domain, MADynamicProgramming planner, PolicyFromJointPolicy policy){
		this.domain = domain;
		this.plannerFactory = new MADPPlannerFactory.ConstantMADPPlannerFactory(planner);
		this.policy = policy;
	}
	
	
	/**
	 * Initializes
	 * @param domain the domain for the agents
	 * @param plannerFactory the valueFunction factory that will be used to generate a valueFunction for the agents
	 * @param policy the policy that will be copied and supplied to all generated objects
	 */
	public MADPPlanAgentFactory(SGDomain domain, MADPPlannerFactory plannerFactory, PolicyFromJointPolicy policy){
		this.domain = domain;
		this.plannerFactory = plannerFactory;
		this.policy = policy;
	}
	
	@Override
	public SGAgent generateAgent(String agentName, SGAgentType type) {
		return new MultiAgentDPPlanningAgent(domain, this.plannerFactory.getPlannerInstance(), this.policy.copy(), agentName, type);
	}

}