clean-up, rotation weight fix
This commit is contained in:
Makrellka
parent
718362190f
commit
d72364e069
@ -11,7 +11,7 @@ from PatchAgent import PatchAgent
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from PatchType import PatchType
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from data.GameConstants import GameConstants
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from decision.ActionType import ActionType
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from pathfinding.Pathfinder import PathFinderOnStates, PathFinderState
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from pathfinding.PathfinderOnStates import PathFinderOnStates, PathFinderState
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from util.PathDefinitions import GridLocation, GridWithWeights
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@ -43,7 +43,7 @@ class GameModel(Model):
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self.grid.place_agent(self.forklift_agent, (x, y))
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self.forklift_agent.current_position = (x, y)
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start, goal = (x, y), (2, 3)
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start, goal = (x, y), (1, 3)
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pathFinder = PathFinderOnStates(
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self.game_constants,
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@ -56,7 +56,7 @@ class GameModel(Model):
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self.place_walls_agents(graph.walls)
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self.place_puddles(graph2.puddles)
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actions = pathFinder.getActionList()
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actions = pathFinder.get_action_list()
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print("PATHFINDING")
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print(actions)
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self.forklift_agent.queue_movement_actions(actions)
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@ -22,7 +22,4 @@ class StateTree:
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)
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def expansion(self, from_state: State) -> List[State]:
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return []
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# a* na przestrzeni stanow
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# heura -> np manhatan
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# funkcja kosztu zroznicowany
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return []
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2
main.py
2
main.py
@ -61,7 +61,7 @@ if __name__ == '__main__':
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diagram4.walls = [(6, 5), (6, 6), (6, 7), (6, 8), (2, 3), (2, 4), (3, 4), (4, 4), (6, 4)]
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diagram5 = GridWithWeights(gridWidth, gridHeight)
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diagram5.puddles = [(2, 2), (2, 5), (5, 4)]
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diagram5.puddles = [(2, 2), (2, 5), (2, 6), (5, 4)]
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grid = CanvasGrid(agent_portrayal, gridWidth, gridHeight, scale * gridWidth, scale * gridHeight)
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17
pathfinding/PathFinderState.py
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17
pathfinding/PathFinderState.py
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@ -0,0 +1,17 @@
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from typing import List
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from data.Direction import Direction
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from decision.ActionType import ActionType
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from util.PathDefinitions import GridLocation
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class PathFinderState:
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def __init__(self, agent_position: GridLocation, agent_direction: Direction, cost: float,
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last_action: ActionType, action_taken: List[ActionType]):
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super().__init__()
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self.agent_position = agent_position
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self.agent_direction = agent_direction
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self.cost = cost
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self.last_action = last_action
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self.action_taken = action_taken
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@ -1,147 +0,0 @@
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from dataclasses import dataclass, field
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from typing import List, Any
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from typing import Tuple
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from data.Direction import Direction
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from data.GameConstants import GameConstants
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from decision.ActionType import ActionType
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from util.PathDefinitions import GridLocation
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from util.PriorityQueue import PriorityQueue
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@dataclass(order=True)
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class PrioritizedItem:
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priority: float
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item: Any = field(compare=False)
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class PathFinderState:
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def __init__(self, agent_position: GridLocation, agent_direction: Direction, cost: float,
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last_action: ActionType, action_taken: List[ActionType]):
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super().__init__()
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self.agent_position = agent_position
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self.agent_direction = agent_direction
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self.cost = cost
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self.last_action = last_action
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self.action_taken = action_taken
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class PathFinderOnStates:
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def __init__(self, game_constants: GameConstants, goal: GridLocation, root_state: PathFinderState):
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super().__init__()
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self.game_constants = game_constants
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self.goal = goal
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self.queue = PriorityQueue()
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self.queue.put(PrioritizedItem(root_state.cost, root_state), root_state.cost)
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def heuristic(self, a: Tuple[int, int], b: Tuple[int, int]) -> float:
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# tutaj mozna uzyc heury np. manhatan distance (zmodyfikowany bo masz obroty a to zmienia oplacalnosc)
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(x1, y1) = a
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(x2, y2) = b
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return abs(x1 - x2) + abs(y1 - y2)
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def evaluate(self, currState: PathFinderState) -> float:
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# koszt dojscia do danego stanu+ heura
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return currState.cost + self.heuristic(currState.agent_position, self.goal)
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def getPositionAfterMove(self, currState: PathFinderState) -> GridLocation:
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if currState.agent_direction == Direction.top:
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return currState.agent_position[0], currState.agent_position[1] + 1
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elif currState.agent_direction == Direction.down:
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return currState.agent_position[0], currState.agent_position[1] - 1
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elif currState.agent_direction == Direction.right:
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return currState.agent_position[0] + 1, currState.agent_position[1]
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elif currState.agent_direction == Direction.left:
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return currState.agent_position[0] - 1, currState.agent_position[1]
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def isMovePossible(self, currState: PathFinderState) -> bool:
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positionAfterMove = self.getPositionAfterMove(currState)
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if positionAfterMove in self.game_constants.walls:
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return False
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elif positionAfterMove[0] < 0 or positionAfterMove[0] > self.game_constants.grid_width:
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return False
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elif positionAfterMove[1] < 0 or positionAfterMove[1] > self.game_constants.grid_height:
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return False
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else:
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return True
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def createState(self, currState: PathFinderState, action: ActionType) -> PathFinderState:
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if currState.agent_position in self.game_constants.diffTerrain:
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cost = currState.cost + 5
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else:
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cost = currState.cost + 1
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last_action = action
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action_taken: List[ActionType] = []
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action_taken.extend(currState.action_taken)
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action_taken.append(last_action)
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agent_position = currState.agent_position
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agent_direction = currState.agent_direction
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if action == ActionType.ROTATE_UP:
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agent_direction = Direction.top
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elif action == ActionType.ROTATE_DOWN:
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agent_direction = Direction.down
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elif action == ActionType.ROTATE_LEFT:
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agent_direction = Direction.left
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elif action == ActionType.ROTATE_RIGHT:
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agent_direction = Direction.right
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elif action == ActionType.MOVE:
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agent_position = self.getPositionAfterMove(currState)
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return PathFinderState(agent_position, agent_direction, cost, last_action, action_taken)
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def expansion(self, currState: PathFinderState) -> List[PathFinderState]:
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# dla stanu sprawdzamy jakie akcje z tego miejsca mozemy podjac (ActionType)
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# reprezentacja kazdego stanu co moge podjac z tego miejsca
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# generowanie stanu
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# sprawdz w ktorym kierunku obrocony
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possibleNextStates: List[PathFinderState] = []
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if self.isMovePossible(currState):
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possibleNextStates.append(self.createState(currState, ActionType.MOVE))
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if currState.agent_direction == Direction.top:
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possibleNextStates.append(self.createState(currState, ActionType.ROTATE_RIGHT))
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possibleNextStates.append(self.createState(currState, ActionType.ROTATE_LEFT))
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possibleNextStates.append(self.createState(currState, ActionType.ROTATE_DOWN))
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elif currState.agent_direction == Direction.down:
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possibleNextStates.append(self.createState(currState, ActionType.ROTATE_RIGHT))
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possibleNextStates.append(self.createState(currState, ActionType.ROTATE_LEFT))
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possibleNextStates.append(self.createState(currState, ActionType.ROTATE_UP))
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elif currState.agent_direction == Direction.left:
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possibleNextStates.append(self.createState(currState, ActionType.ROTATE_RIGHT))
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possibleNextStates.append(self.createState(currState, ActionType.ROTATE_UP))
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possibleNextStates.append(self.createState(currState, ActionType.ROTATE_DOWN))
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elif currState.agent_direction == Direction.right:
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possibleNextStates.append(self.createState(currState, ActionType.ROTATE_UP))
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possibleNextStates.append(self.createState(currState, ActionType.ROTATE_LEFT))
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possibleNextStates.append(self.createState(currState, ActionType.ROTATE_DOWN))
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return possibleNextStates
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def getActionList(self) -> List[ActionType]:
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already_visited = {}
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while not self.queue.empty():
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item: PrioritizedItem = self.queue.get()
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best_state: PathFinderState = item.item
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if best_state.agent_position == self.goal or (self.heuristic(best_state.agent_position, self.goal) == 1
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and self.goal in self.game_constants.walls):
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break
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for state in self.expansion(best_state):
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s_tuple = (state.agent_position[0], state.agent_position[1], state.agent_direction)
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if s_tuple not in already_visited:
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priority = self.evaluate(state)
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self.queue.put(PrioritizedItem(priority, state), priority)
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already_visited[s_tuple] = state
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return best_state.action_taken
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138
pathfinding/PathfinderOnStates.py
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138
pathfinding/PathfinderOnStates.py
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@ -0,0 +1,138 @@
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from typing import List
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from typing import Tuple
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from data.Direction import Direction
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from data.GameConstants import GameConstants
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from decision.ActionType import ActionType
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from pathfinding.PathFinderState import PathFinderState
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from pathfinding.PrioritizedItem import PrioritizedItem
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from util.PathDefinitions import GridLocation
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from util.PriorityQueue import PriorityQueue
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class PathFinderOnStates:
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def __init__(self, game_constants: GameConstants, goal: GridLocation, root_state: PathFinderState):
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super().__init__()
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self.game_constants = game_constants
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self.goal = goal
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self.queue = PriorityQueue()
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self.queue.put(PrioritizedItem(root_state.cost, root_state), root_state.cost)
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def heuristic(self, a: Tuple[int, int], b: Tuple[int, int]) -> float:
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# tutaj mozna uzyc heury np. manhatan distance (zmodyfikowany bo masz obroty a to zmienia oplacalnosc)
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(x1, y1) = a
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(x2, y2) = b
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return abs(x1 - x2) + abs(y1 - y2)
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def evaluate(self, curr_state: PathFinderState) -> float:
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# koszt dojscia do danego stanu+ heura
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return curr_state.cost + self.heuristic(curr_state.agent_position, self.goal)
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def get_position_after_move(self, curr_state: PathFinderState) -> GridLocation:
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if curr_state.agent_direction == Direction.top:
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return curr_state.agent_position[0], curr_state.agent_position[1] + 1
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elif curr_state.agent_direction == Direction.down:
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return curr_state.agent_position[0], curr_state.agent_position[1] - 1
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elif curr_state.agent_direction == Direction.right:
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return curr_state.agent_position[0] + 1, curr_state.agent_position[1]
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elif curr_state.agent_direction == Direction.left:
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return curr_state.agent_position[0] - 1, curr_state.agent_position[1]
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def is_move_possible(self, curr_state: PathFinderState) -> bool:
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position_after_move = self.get_position_after_move(curr_state)
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if position_after_move in self.game_constants.walls:
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return False
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elif position_after_move[0] < 0 or position_after_move[0] > self.game_constants.grid_width:
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return False
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elif position_after_move[1] < 0 or position_after_move[1] > self.game_constants.grid_height:
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return False
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else:
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return True
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def create_state(self, curr_state: PathFinderState, action: ActionType) -> PathFinderState:
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if action == action.MOVE:
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if curr_state.agent_position in self.game_constants.diffTerrain:
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cost = curr_state.cost + 20
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# tutaj koszt kaluzy
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else:
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cost = curr_state.cost + 1
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#TODO: jezeli bedziemy rozpatrywac rozne stany to nalezy rozbic na kazdy mozliwy obrot
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else:
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cost = curr_state.cost + 10
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last_action = action
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action_taken: List[ActionType] = []
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action_taken.extend(curr_state.action_taken)
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action_taken.append(last_action)
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agent_position = curr_state.agent_position
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agent_direction = curr_state.agent_direction
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if action == ActionType.ROTATE_UP:
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agent_direction = Direction.top
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elif action == ActionType.ROTATE_DOWN:
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agent_direction = Direction.down
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elif action == ActionType.ROTATE_LEFT:
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agent_direction = Direction.left
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elif action == ActionType.ROTATE_RIGHT:
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agent_direction = Direction.right
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elif action == ActionType.MOVE:
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agent_position = self.get_position_after_move(curr_state)
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return PathFinderState(agent_position, agent_direction, cost, last_action, action_taken)
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# Funkcja następnika
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def expansion(self, curr_state: PathFinderState) -> List[PathFinderState]:
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# dla stanu sprawdzamy jakie akcje z tego miejsca mozemy podjac (ActionType)
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# reprezentacja kazdego stanu co moge podjac z tego miejsca
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# generowanie stanu
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# sprawdz w ktorym kierunku obrocony
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possible_next_states: List[PathFinderState] = []
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if self.is_move_possible(curr_state):
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possible_next_states.append(self.create_state(curr_state, ActionType.MOVE))
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if curr_state.agent_direction == Direction.top:
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possible_next_states.append(self.create_state(curr_state, ActionType.ROTATE_RIGHT))
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possible_next_states.append(self.create_state(curr_state, ActionType.ROTATE_LEFT))
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possible_next_states.append(self.create_state(curr_state, ActionType.ROTATE_DOWN))
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elif curr_state.agent_direction == Direction.down:
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possible_next_states.append(self.create_state(curr_state, ActionType.ROTATE_RIGHT))
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possible_next_states.append(self.create_state(curr_state, ActionType.ROTATE_LEFT))
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possible_next_states.append(self.create_state(curr_state, ActionType.ROTATE_UP))
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elif curr_state.agent_direction == Direction.left:
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possible_next_states.append(self.create_state(curr_state, ActionType.ROTATE_RIGHT))
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possible_next_states.append(self.create_state(curr_state, ActionType.ROTATE_UP))
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possible_next_states.append(self.create_state(curr_state, ActionType.ROTATE_DOWN))
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elif curr_state.agent_direction == Direction.right:
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possible_next_states.append(self.create_state(curr_state, ActionType.ROTATE_UP))
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possible_next_states.append(self.create_state(curr_state, ActionType.ROTATE_LEFT))
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possible_next_states.append(self.create_state(curr_state, ActionType.ROTATE_DOWN))
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return possible_next_states
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def get_action_list(self) -> List[ActionType]:
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already_visited = {}
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while not self.queue.empty():
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item: PrioritizedItem = self.queue.get()
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best_state: PathFinderState = item.item
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if best_state.agent_position == self.goal or (self.heuristic(best_state.agent_position, self.goal) == 1
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and self.goal in self.game_constants.walls):
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break
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for state in self.expansion(best_state):
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s_tuple = (state.agent_position[0], state.agent_position[1], state.agent_direction)
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if s_tuple not in already_visited:
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priority = self.evaluate(state)
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self.queue.put(PrioritizedItem(priority, state), priority)
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already_visited[s_tuple] = state
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return best_state.action_taken
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8
pathfinding/PrioritizedItem.py
Normal file
8
pathfinding/PrioritizedItem.py
Normal file
@ -0,0 +1,8 @@
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from dataclasses import dataclass, field
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from typing import Any
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@dataclass(order=True)
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class PrioritizedItem:
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priority: float
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item: Any = field(compare=False)
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@ -1,43 +0,0 @@
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# thanks to @m1sp <Jaiden Mispy> for this simpler version of
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# reconstruct_path that doesn't have duplicate entries
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from typing import Dict, List
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from util.Pathfinder import Location
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def reconstruct_path(came_from: Dict[Location, Location],
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start: Location, goal: Location) -> List[Location]:
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current: Location = goal
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path: List[Location] = []
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while current != start: # note: this will fail if no path found
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path.append(current)
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current = came_from[current]
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path.append(start) # optional
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path.reverse() # optional
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return path
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def draw_grid(graph, **style):
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print("___" * graph.width)
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for y in range(graph.height):
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for x in range(graph.width):
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print("%s" % draw_tile(graph, (x, y), style), end="")
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print()
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print("~~~" * graph.width)
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def draw_tile(graph, id, style):
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r = " . "
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if 'number' in style and id in style['number']: r = " %-2d" % style['number'][id]
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if 'point_to' in style and style['point_to'].get(id, None) is not None:
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(x1, y1) = id
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(x2, y2) = style['point_to'][id]
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if x2 == x1 + 1: r = " > "
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if x2 == x1 - 1: r = " < "
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if y2 == y1 + 1: r = " v "
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if y2 == y1 - 1: r = " ^ "
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if 'path' in style and id in style['path']: r = " @ "
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if 'start' in style and id == style['start']: r = " A "
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if 'goal' in style and id == style['goal']: r = " Z "
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if id in graph.walls: r = "###"
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return r
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@ -1,36 +0,0 @@
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from typing import Optional, Dict, Tuple
|
||||
|
||||
from util.PathDefinitions import WeightedGraph, Location
|
||||
from util.PriorityQueue import PriorityQueue
|
||||
|
||||
|
||||
def heuristic(a: Tuple[int, int], b: Tuple[int, int]) -> float:
|
||||
(x1, y1) = a
|
||||
|
||||
(x2, y2) = b
|
||||
return abs(x1 - x2) + abs(y1 - y2)
|
||||
|
||||
|
||||
def a_star_search(graph: WeightedGraph, start: Tuple[int, int], goal: Tuple[int, int]):
|
||||
frontier = PriorityQueue()
|
||||
frontier.put(start, 0)
|
||||
came_from: Dict[Location, Optional[Location]] = {}
|
||||
cost_so_far: Dict[Location, float] = {}
|
||||
came_from[start] = None
|
||||
cost_so_far[start] = 0
|
||||
|
||||
while not frontier.empty():
|
||||
current: Location = frontier.get()
|
||||
|
||||
if current == goal:
|
||||
break
|
||||
|
||||
for next in graph.neighbors(current):
|
||||
new_cost = cost_so_far[current] + graph.cost(current, next)
|
||||
if next not in cost_so_far or new_cost < cost_so_far[next]:
|
||||
cost_so_far[next] = new_cost
|
||||
priority = new_cost + heuristic(next, goal)
|
||||
frontier.put(next, priority)
|
||||
came_from[next] = current
|
||||
|
||||
return came_from, cost_so_far.keys()
|
||||
Loading…
Reference in New Issue
Block a user