burlap.behavior.singleagent.planning.deterministic.DeterministicPlanner Java Examples

The following examples show how to use burlap.behavior.singleagent.planning.deterministic.DeterministicPlanner. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may check out the related API usage on the sidebar.
Example #1
Source File: TestPlanning.java    From burlap with Apache License 2.0 5 votes vote down vote up 
@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);
}
Example #2
Source File: TestPlanning.java    From burlap with Apache License 2.0 5 votes vote down vote up 
@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);
}
Example #3
Source File: TestPlanning.java    From burlap with Apache License 2.0 5 votes vote down vote up 
@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);
}
Example #4
Source File: BasicBehavior.java    From burlap_examples with MIT License 4 votes vote down vote up 
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");

	}
Example #5
Source File: BasicBehavior.java    From burlap_examples with MIT License 3 votes vote down vote up 
public void BFSExample(String outputPath){

		DeterministicPlanner planner = new BFS(domain, goalCondition, hashingFactory);
		Policy p = planner.planFromState(initialState);
		PolicyUtils.rollout(p, initialState, domain.getModel()).write(outputPath + "bfs");

	}
Example #6
Source File: BasicBehavior.java    From burlap_examples with MIT License 3 votes vote down vote up 
public void DFSExample(String outputPath){

		DeterministicPlanner planner = new DFS(domain, goalCondition, hashingFactory);
		Policy p = planner.planFromState(initialState);
		PolicyUtils.rollout(p, initialState, domain.getModel()).write(outputPath + "dfs");

	}