burlap.domain.singleagent.gridworld.state.GridAgent Java Examples

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);
    }
 

/**
 * Creates a grid world state with the agent in (0,0) and various different grid cell types scattered about.
 * @return a grid world state with the agent in (0,0) and various different grid cell types scattered about.
 */
protected State basicState(){

	GridWorldState s = new GridWorldState(
			new GridAgent(0, 0),
			new GridLocation(0, 0, 1, "loc0"),
			new GridLocation(0, 4, 2, "loc1"),
			new GridLocation(4, 4, 3, "loc2"),
			new GridLocation(4, 0, 4, "loc3"),

			new GridLocation(1, 0, 0, "loc4"),
			new GridLocation(1, 2, 0, "loc5"),
			new GridLocation(1, 4, 0, "loc6"),
			new GridLocation(3, 1, 0, "loc7"),
			new GridLocation(3, 3, 0, "loc8")
	);

	return s;
}
 

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();

	}
 

/**
 * Attempts to move the agent into the given position, taking into account walls and blocks
 * @param s the current state
 * @param xd the attempted new X position of the agent
 * @param yd the attempted new Y position of the agent
 * @return input state s, after modification
 */
protected State move(State s, int xd, int yd){

	GridWorldState gws = (GridWorldState)s;

	int ax = gws.agent.x;
	int ay = gws.agent.y;

	int nx = ax+xd;
	int ny = ay+yd;

	//hit wall, so do not change position
	if(nx < 0 || nx >= map.length || ny < 0 || ny >= map[0].length || map[nx][ny] == 1 ||
			(xd > 0 && (map[ax][ay] == 3 || map[ax][ay] == 4)) || (xd < 0 && (map[nx][ny] == 3 || map[nx][ny] == 4)) ||
			(yd > 0 && (map[ax][ay] == 2 || map[ax][ay] == 4)) || (yd < 0 && (map[nx][ny] == 2 || map[nx][ny] == 4)) ){
		nx = ax;
		ny = ay;
	}

	GridAgent nagent = gws.touchAgent();
	nagent.x = nx;
	nagent.y = ny;

	return s;
}
 

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);

	}
 

@Test
public void testBFS() {
	GridWorldState initialState = new GridWorldState(new GridAgent(0, 0), new GridLocation(10, 10, 0, "loc0"));

	DeterministicPlanner planner = new BFS(this.domain, this.goalCondition, this.hashingFactory);
	planner.planFromState(initialState);
	Policy p = new SDPlannerPolicy(planner);
	Episode analysis = rollout(p, initialState, domain.getModel());
	this.evaluateEpisode(analysis, true);
}
 

@Test
public void testDFS() {
	GridWorldState initialState = new GridWorldState(new GridAgent(0, 0), new GridLocation(10, 10, 0, "loc0"));
	
	DeterministicPlanner planner = new DFS(this.domain, this.goalCondition, this.hashingFactory, -1 , true);
	planner.planFromState(initialState);
	Policy p = new SDPlannerPolicy(planner);
	Episode analysis = rollout(p, initialState, domain.getModel());
	this.evaluateEpisode(analysis);
}
 

@Test
public void testAStar() {
	GridWorldState initialState = new GridWorldState(new GridAgent(0, 0), new GridLocation(10, 10, 0, "loc0"));
	
	Heuristic mdistHeuristic = new Heuristic() {
		
		@Override
		public double h(State s) {

			GridAgent agent = ((GridWorldState)s).agent;
			GridLocation location = ((GridWorldState)s).locations.get(0);

			//get agent position
			int ax = agent.x;
			int ay = agent.y;
			
			//get location position
			int lx = location.x;
			int ly = location.y;
			
			//compute Manhattan distance
			double mdist = Math.abs(ax-lx) + Math.abs(ay-ly);
			
			return -mdist;
		}
	};
	
	//provide A* the heuristic as well as the reward function so that it can keep
	//track of the actual cost
	DeterministicPlanner planner = new AStar(domain, goalCondition,
		hashingFactory, mdistHeuristic);
	planner.planFromState(initialState);
	Policy p = new SDPlannerPolicy(planner);
	
	Episode analysis = PolicyUtils.rollout(p, initialState, domain.getModel());
	this.evaluateEpisode(analysis, true);
}
 

public State generateLargeGW(SADomain domain, int width) {

		GridWorldState state = new GridWorldState(new GridAgent());

		for (int i = 0; i < width; i++) {
			state.locations.add(new GridLocation(i, width - 1 - i, "loc"+i));
		}
		return state;
	}
 

public void AStarExample(String outputPath){

		Heuristic mdistHeuristic = new Heuristic() {

			public double h(State s) {
				GridAgent a = ((GridWorldState)s).agent;
				double mdist = Math.abs(a.x-10) + Math.abs(a.y-10);

				return -mdist;
			}
		};

		DeterministicPlanner planner = new AStar(domain, goalCondition, hashingFactory, mdistHeuristic);
		Policy p = planner.planFromState(initialState);

		PolicyUtils.rollout(p, initialState, domain.getModel()).write(outputPath + "astar");

	}
 

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());

}
 

/**
 * Creates a visual explorer or terminal explorer. By default a visual explorer is presented; use the "t" argument
 * to create terminal explorer. Will create a 4 rooms grid world with the agent in lower left corner and a location in
 * the upper right. Use w-a-s-d to move.
 * @param args command line args
 */
public static void main(String[] args) {

	GridWorldDomain gwdg = new GridWorldDomain(11, 11);
	gwdg.setMapToFourRooms();
	//gwdg.setProbSucceedTransitionDynamics(0.75);

	SADomain d = gwdg.generateDomain();


	GridWorldState s = new GridWorldState(new GridAgent(0, 0), new GridLocation(10, 10, "loc0"));

	
	int expMode = 1;
	if(args.length > 0){
		if(args[0].equals("v")){
			expMode = 1;
		}
		else if(args[0].equals("t")){
			expMode = 0;
		}
	}
	
	if(expMode == 0){

		EnvironmentShell shell = new EnvironmentShell(d, s);
		shell.start();
		
	}
	else if(expMode == 1){
		
		Visualizer v = GridWorldVisualizer.getVisualizer(gwdg.getMap());
		VisualExplorer exp = new VisualExplorer(d, v, s);
		
		//use w-s-a-d-x
		exp.addKeyAction("w", ACTION_NORTH, "");
		exp.addKeyAction("s", ACTION_SOUTH, "");
		exp.addKeyAction("a", ACTION_WEST, "");
		exp.addKeyAction("d", ACTION_EAST, "");
		
		exp.initGUI();
	}
	
	
}
 

public State generateState() {
	GridWorldState s = new GridWorldState(new GridAgent(0, 0), new GridLocation(10, 10, "location0"));
	return s;
}
 

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();


	}