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

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 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();


	}