Java Code Examples for burlap.domain.singleagent.gridworld.GridWorldDomain#generateDomain()

public GridWorldDQN(String solverFile, double gamma) {

        //create the domain
        gwdg = new GridWorldDomain(11, 11);
        gwdg.setMapToFourRooms();
        rf = new UniformCostRF();
        tf = new SinglePFTF(PropositionalFunction.findPF(gwdg.generatePfs(), GridWorldDomain.PF_AT_LOCATION));
        gwdg.setRf(rf);
        gwdg.setTf(tf);
        domain = gwdg.generateDomain();

        goalCondition = new TFGoalCondition(tf);

        //set up the initial state of the task
        initialState = new GridWorldState(new GridAgent(0, 0), new GridLocation(10, 10, "loc0"));

        //set up the state hashing system for tabular algorithms
        hashingFactory = new SimpleHashableStateFactory();

        //set up the environment for learners algorithms
        env = new SimulatedEnvironment(domain, initialState);

        dqn = new DQN(solverFile, actionSet, new NNGridStateConverter(), gamma);
    }
 

public BasicBehavior(){
		gwdg = new GridWorldDomain(11, 11);
		gwdg.setMapToFourRooms();
		tf = new GridWorldTerminalFunction(10, 10);
		gwdg.setTf(tf);
		goalCondition = new TFGoalCondition(tf);
		domain = gwdg.generateDomain();

		initialState = new GridWorldState(new GridAgent(0, 0), new GridLocation(10, 10, "loc0"));
		hashingFactory = new SimpleHashableStateFactory();

		env = new SimulatedEnvironment(domain, initialState);


//		VisualActionObserver observer = new VisualActionObserver(domain, GridWorldVisualizer.getVisualizer(gwdg.getMap()));
//		observer.initGUI();
//		env.addObservers(observer);
	}
 

public static void main(String[] args) {

		GridWorldDomain gw = new GridWorldDomain(11,11); //11x11 grid world
		gw.setMapToFourRooms(); //four rooms layout
		gw.setProbSucceedTransitionDynamics(0.8); //stochastic transitions with 0.8 success rate
		SADomain domain = gw.generateDomain(); //generate the grid world domain

		//setup initial state
		State s = new GridWorldState(new GridAgent(0, 0), new GridLocation(10, 10, "loc0"));

		//create visualizer and explorer
		Visualizer v = GridWorldVisualizer.getVisualizer(gw.getMap());
		VisualExplorer exp = new VisualExplorer(domain, v, s);

		//set control keys to use w-s-a-d
		exp.addKeyAction("w", GridWorldDomain.ACTION_NORTH, "");
		exp.addKeyAction("s", GridWorldDomain.ACTION_SOUTH, "");
		exp.addKeyAction("a", GridWorldDomain.ACTION_WEST, "");
		exp.addKeyAction("d", GridWorldDomain.ACTION_EAST, "");

		exp.initGUI();

	}
 

public static void main(String [] args){

		GridWorldDomain gwd = new GridWorldDomain(11, 11);
		gwd.setTf(new GridWorldTerminalFunction(10, 10));
		gwd.setMapToFourRooms();

		//only go in intended directon 80% of the time
		gwd.setProbSucceedTransitionDynamics(0.8);

		SADomain domain = gwd.generateDomain();

		//get initial state with agent in 0,0
		State s = new GridWorldState(new GridAgent(0, 0));

		//setup vi with 0.99 discount factor, a value
		//function initialization that initializes all states to value 0, and which will
		//run for 30 iterations over the state space
		VITutorial vi = new VITutorial(domain, 0.99, new SimpleHashableStateFactory(),
				new ConstantValueFunction(0.0), 30);

		//run planning from our initial state
		Policy p = vi.planFromState(s);

		//evaluate the policy with one roll out visualize the trajectory
		Episode ea = PolicyUtils.rollout(p, s, domain.getModel());

		Visualizer v = GridWorldVisualizer.getVisualizer(gwd.getMap());
		new EpisodeSequenceVisualizer(v, domain, Arrays.asList(ea));

	}
 

public static void main(String[] args) {

		GridWorldDomain gwd = new GridWorldDomain(11, 11);
		gwd.setMapToFourRooms();
		gwd.setProbSucceedTransitionDynamics(0.8);
		gwd.setTf(new GridWorldTerminalFunction(10, 10));

		SADomain domain = gwd.generateDomain();

		//get initial state with agent in 0,0
		State s = new GridWorldState(new GridAgent(0, 0));

		//create environment
		SimulatedEnvironment env = new SimulatedEnvironment(domain, s);

		//create Q-learning
		QLTutorial agent = new QLTutorial(domain, 0.99, new SimpleHashableStateFactory(),
				new ConstantValueFunction(), 0.1, 0.1);

		//run Q-learning and store results in a list
		List<Episode> episodes = new ArrayList<Episode>(1000);
		for(int i = 0; i < 1000; i++){
			episodes.add(agent.runLearningEpisode(env));
			env.resetEnvironment();
		}

		Visualizer v = GridWorldVisualizer.getVisualizer(gwd.getMap());
		new EpisodeSequenceVisualizer(v, domain, episodes);

	}
 

public void testSimpleHashFactoryLargeState(HashableStateFactory factory, int width, int numRandomStates, boolean moveLocObjects) {
	GridWorldDomain gw = new GridWorldDomain(width, width);
	SADomain domain = (SADomain)gw.generateDomain();
	State startState = this.generateLargeGW(domain, width);
	Set<HashableState> hashedStates = this.generateRandomStates(domain, startState, factory, width, numRandomStates, moveLocObjects);
	Set<Integer> hashes = new HashSet<Integer>();
	for (HashableState hs : hashedStates) {
		hashes.add(hs.hashCode());
	}
	System.out.println("Hashed states: " + hashedStates.size() + ", hashes: " + hashes.size());
}
 

public static void main(String[] args) {
	GridWorldDomain gwd = new GridWorldDomain(11, 11);
	SADomain domain = gwd.generateDomain();
	State s = new GridWorldState(new GridAgent(1, 3));

	Policy p = new RandomPolicy(domain);
	Episode ea = PolicyUtils.rollout(p, s, domain.getModel(), 30);

	String yamlOut = ea.serialize();

	System.out.println(yamlOut);

	System.out.println("\n\n");

	Episode read = Episode.parseEpisode(yamlOut);

	System.out.println(read.actionString());
	System.out.println(read.state(0).toString());
	System.out.println(read.actionSequence.size());
	System.out.println(read.stateSequence.size());

}
 

public static void testOptions(){

		GridWorldDomain gwd = new GridWorldDomain(11, 11);
		gwd.setMapToFourRooms();
		SADomain domain = gwd.generateDomain();

		Option swToNorth = createRoomOption("swToNorth", domain, 1, 5, 0, 0, 4, 4);
		Option swToEast = createRoomOption("swToEast", domain, 5, 1, 0, 0, 4, 4);

		Option seToWest = createRoomOption("seToWest", domain, 5, 1, 6, 0, 10, 3);
		Option seToNorth = createRoomOption("seToNorth", domain, 8, 4, 6, 0, 10, 3);

		Option neToSouth = createRoomOption("neToSouth", domain, 8, 4, 6, 5, 10, 10);
		Option neToWest = createRoomOption("neToWest", domain, 5, 8, 6, 5, 10, 10);

		Option nwToEast = createRoomOption("nwToEast", domain, 5, 8, 0, 6, 4, 10);
		Option nwToSouth = createRoomOption("nwToSouth", domain, 1, 5, 0, 6, 4, 10);

		List<Episode> episodes = new ArrayList<Episode>();

		episodes.add(optionExecuteResult(domain, swToNorth, new GridWorldState(0, 0)));
		episodes.add(optionExecuteResult(domain, swToEast, new GridWorldState(0, 0)));

		episodes.add(optionExecuteResult(domain, seToWest, new GridWorldState(10, 0)));
		episodes.add(optionExecuteResult(domain, seToNorth, new GridWorldState(10, 0)));

		episodes.add(optionExecuteResult(domain, neToSouth, new GridWorldState(10, 10)));
		episodes.add(optionExecuteResult(domain, neToWest, new GridWorldState(10, 10)));

		episodes.add(optionExecuteResult(domain, nwToEast, new GridWorldState(0, 10)));
		episodes.add(optionExecuteResult(domain, nwToSouth, new GridWorldState(0, 10)));


		Visualizer v = GridWorldVisualizer.getVisualizer(gwd.getMap());
		EpisodeSequenceVisualizer evis = new EpisodeSequenceVisualizer(v, domain, episodes);


	}
 

public static void main(String [] args){

		GridWorldDomain gw = new GridWorldDomain(11,11); //11x11 grid world
		gw.setMapToFourRooms(); //four rooms layout
		gw.setProbSucceedTransitionDynamics(0.8); //stochastic transitions with 0.8 success rate

		//ends when the agent reaches a location
		final TerminalFunction tf = new SinglePFTF(
				PropositionalFunction.findPF(gw.generatePfs(), GridWorldDomain.PF_AT_LOCATION));

		//reward function definition
		final RewardFunction rf = new GoalBasedRF(new TFGoalCondition(tf), 5., -0.1);

		gw.setTf(tf);
		gw.setRf(rf);


		final OOSADomain domain = gw.generateDomain(); //generate the grid world domain

		//setup initial state
		GridWorldState s = new GridWorldState(new GridAgent(0, 0), new GridLocation(10, 10, "loc0"));



		//initial state generator
		final ConstantStateGenerator sg = new ConstantStateGenerator(s);


		//set up the state hashing system for looking up states
		final SimpleHashableStateFactory hashingFactory = new SimpleHashableStateFactory();


		/**
		 * Create factory for Q-learning agent
		 */
		LearningAgentFactory qLearningFactory = new LearningAgentFactory() {

			public String getAgentName() {
				return "Q-learning";
			}

			public LearningAgent generateAgent() {
				return new QLearning(domain, 0.99, hashingFactory, 0.3, 0.1);
			}
		};

		//define learning environment
		SimulatedEnvironment env = new SimulatedEnvironment(domain, sg);

		//define experiment
		LearningAlgorithmExperimenter exp = new LearningAlgorithmExperimenter(env,
				10, 100, qLearningFactory);

		exp.setUpPlottingConfiguration(500, 250, 2, 1000, TrialMode.MOST_RECENT_AND_AVERAGE,
				PerformanceMetric.CUMULATIVE_STEPS_PER_EPISODE, PerformanceMetric.AVERAGE_EPISODE_REWARD);


		//start experiment
		exp.startExperiment();


	}