def get_next_optimal_state(self):
from MAPFSolver.Utilities.AStar import AStar
from MAPFSolver.Utilities.SolverSettings import SolverSettings
solver = AStar(SolverSettings())
path = solver.find_path(self._map, self._position, self._goal)
next_pos = path[1]
return SingleAgentState(self._map,
self._goal,
next_pos,
self._solver_settings,
parent=self)
def get_next_optimal_state(self):
"""
Compute the next optimal state following the optimal policy.
:return: the next state following the optimal policy
"""
from MAPFSolver.Utilities.AStar import AStar
from MAPFSolver.Utilities.SolverSettings import SolverSettings
solver = AStar(SolverSettings())
path = solver.find_path(self._map, self._position, self._goal)
next_pos = path[1]
return SingleAgentState(self._map,
self._goal,
next_pos,
self._solver_settings,
parent=self)
def compute_optimal_costs_vector(self):
"""
Returns the the optimal costs vector. It will have all the optimal costs for each agent.
"""
path_costs_vector = []
solver = AStar(self._solver_settings)
for agent in self._problem_instance.get_agents():
path = solver.find_path(self._problem_instance.get_map(),
agent.get_start(), agent.get_goal())
if self._solver_settings.stay_at_goal():
cost = len(path) - 1
else:
cost = len(
path) - self._solver_settings.get_goal_occupation_time()
path_costs_vector.append(cost)
return path_costs_vector