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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.shell.command.env;

import burlap.mdp.core.action.Action;
import burlap.mdp.core.Domain;
import burlap.mdp.core.action.ActionType;
import burlap.mdp.singleagent.SADomain;
import burlap.mdp.singleagent.environment.Environment;
import burlap.mdp.singleagent.environment.EnvironmentOutcome;
import burlap.shell.BurlapShell;
import burlap.shell.EnvironmentShell;
import burlap.shell.command.ShellCommand;
import joptsimple.OptionParser;
import joptsimple.OptionSet;

import java.io.PrintStream;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Scanner;

/**
 * A {@link burlap.shell.command.ShellCommand} for executing an action in the {@link burlap.mdp.singleagent.environment.Environment}
 * Use the -h option for help information.
 * @author James MacGlashan.
 */
public class ExecuteActionCommand implements ShellCommand {

	protected OptionParser parser = new OptionParser("vah*");
	protected Map actionNameMap = new HashMap();
	protected Domain domain;

	public ExecuteActionCommand(Domain domain) {
		this.domain = domain;
	}

	@Override
	public String commandName() {
		return "ex";
	}

	@Override
	public int call(BurlapShell shell, String argString, Scanner is, PrintStream os) {

		Environment env = ((EnvironmentShell)shell).getEnv();

		OptionSet oset = this.parser.parse(argString.split(" "));
		List args = (List)oset.nonOptionArguments();

		if(oset.has("h")){
			os.println("[v|a] args*\nCommand to execute an action or set an action name alias.\n" +
					"If -a is not specified, then executes the action with name args[0] with parameters args[1]*\n" +
					"-v: the resulting reward, termination, and observation from execution is printed.\n" +
					"-a: assigns an action name alias where args[0] is the original action name, and args[1] is the alias.");

			return 0;
		}

		if(oset.has("a")){
			if(args.size() != 2){
				return  -1;
			}
			this.actionNameMap.put(args.get(1), args.get(0));
			return 0;
		}

		if(args.isEmpty()){
			return -1;
		}

		ActionType actionType = ((SADomain)this.domain).getAction(args.get(0));
		if(actionType == null){
			String actionName = this.actionNameMap.get(args.get(0));
			if(actionName != null){
				actionType = ((SADomain)this.domain).getAction(actionName);
			}
		}
		if(actionType != null){
			Action a = actionType.associatedAction(actionArgs(args));
			EnvironmentOutcome o = env.executeAction(a);
			if(oset.has("v")){
				os.println("reward: " + o.r);
				if(o.terminated){
					os.println("IS terminal");
				}
				else{
					os.println("is NOT terminal");
				}
				os.println(o.op.toString());
			}
			return 1;
		}


		return -1;
	}

	protected String actionArgs(List commandArgs){
		StringBuilder buf = new StringBuilder();
		for(int i = 1; i < commandArgs.size(); i++){
			if(i > 1){
				buf.append(" ");
			}
			buf.append(commandArgs.get(i));
		}
		return buf.toString();
	}


}