burlap.mdp.stochasticgames.common.AgentFactoryWithSubjectiveReward Maven / Gradle / Ivy

Go to download

Show more of this group Show more artifacts with this name
Show all versions of burlap Show documentation

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.

The newest version!

package burlap.mdp.stochasticgames.common;

import burlap.mdp.stochasticgames.agent.SGAgent;
import burlap.mdp.stochasticgames.agent.AgentFactory;
import burlap.mdp.stochasticgames.agent.SGAgentBase;
import burlap.mdp.stochasticgames.agent.SGAgentType;
import burlap.mdp.stochasticgames.model.JointRewardFunction;

/**
 * An agent generating factory that will produce an agent that uses an internal subjective reward function.
 * This can be useful for agents that use reward shaping. The base agent is first generated using
 * a different {@link AgentFactory} and the returned agent from
 * that provided agent has its internal reward function set to the one specified for use
 * in this factory. The agent is then returned by this factory.
 * @author James MacGlashan
 *
 */
public class AgentFactoryWithSubjectiveReward implements AgentFactory {

	protected AgentFactory			baseFactory;
	protected JointRewardFunction internalReward;
	
	
	/**
	 * Initializes the factory.
	 * @param baseFactory the base factory for generating an agent.
	 * @param internalReward the internal reward function to set the agent to use.
	 */
	public AgentFactoryWithSubjectiveReward(AgentFactory baseFactory, JointRewardFunction internalReward) {
		this.baseFactory = baseFactory;
		this.internalReward = internalReward;
	}

	@Override
	public SGAgent generateAgent(String agentName, SGAgentType type) {
		SGAgentBase a = (SGAgentBase)baseFactory.generateAgent(agentName, type);
		a.setInternalRewardFunction(internalReward);
		return a;
	}

}