def main():
parser = argparse.ArgumentParser()
#parser.add_argument('-p', '--problem', default='problem1', help='The name of the problem to solve')
parser.add_argument('-a', '--algorithm', default=None, help='Specifies the algorithm')
parser.add_argument('-o', '--optimal', action='store_true', help='Runs in an anytime mode')
parser.add_argument('-t', '--max_time', default=2, type=int, help='The max time')
args = parser.parse_args()
print('Arguments:', args)
problem_fn = problem3 # problem1 | problem2 | problem3
stream_pddl, stream_map, init, terms = problem_fn()
#print('Init:', pddlstream_problem.init)
#print('Goal:', pddlstream_problem.goal)
info = {
# Intentionally, misleading the stream
'positive': StreamInfo(overhead=2),
'negative': StreamInfo(overhead=1),
'test-small': StreamInfo(p_success=1e-1),
# Alternatively, can make the second stream called work
}
success_cost = 0 if args.optimal else INF
if args.algorithm == 'focused':
solution = solve_pddlstream_satisfaction(stream_pddl, stream_map, init, terms, incremental=False,
stream_info=info, max_time=args.max_time, success_cost=success_cost)
elif args.algorithm == 'incremental':
solution = solve_pddlstream_satisfaction(stream_pddl, stream_map, init, terms, incremental=True,
max_time=args.max_time, success_cost=success_cost)
else:
solution = constraint_satisfaction(stream_pddl, stream_map, init, terms, stream_info=info,
max_time=args.max_time, success_cost=success_cost)
dump_assignment(solution)
def main(success_cost=0):
parser = argparse.ArgumentParser()
parser.add_argument('-d', '--deterministic', action='store_true', help='Uses a deterministic sampler')
parser.add_argument('-a', '--algorithm', default='', help='Specifies the algorithm')
parser.add_argument('-o', '--optimizer', action='store_true', help='Uses the optimizers')
parser.add_argument('-t', '--max_time', default=30, type=int, help='The max time')
parser.add_argument('-u', '--unit', action='store_true', help='Uses unit costs')
args = parser.parse_args()
print('Arguments:', args)
np.set_printoptions(precision=2)
if args.deterministic:
set_deterministic()
print('Random seed:', get_random_seed())
tamp_problem = get_tight_problem(n_blocks=2, n_goals=2)
print(tamp_problem)
pddlstream_problem = pddlstream_from_tamp(tamp_problem, use_stream=not args.optimizer,
use_optimizer=args.optimizer)
stream_pddl, stream_map = pddlstream_problem[2:4]
stream_info = {
't-region': StreamInfo(eager=True, p_success=0), # bound_fn is None
#'t-cfree': StreamInfo(eager=False, negate=True),
#'distance': FunctionInfo(opt_fn=lambda q1, q2: MOVE_COST), # Doesn't make a difference
}
terms = CONSTRAINTS + OBJECTIVES
pr = cProfile.Profile()
pr.enable()
if args.algorithm == 'focused':
solution = solve_pddlstream_satisfaction(stream_pddl, stream_map, INIT, terms,
incremental=False, stream_info=stream_info,
#search_sample_ratio=1,
#max_skeletons=1,
success_cost=success_cost, max_time=args.max_time)
elif args.algorithm == 'incremental':
solution = solve_pddlstream_satisfaction(stream_pddl, stream_map, INIT, terms, incremental=True,
success_cost=success_cost, max_time=args.max_time,
verbose=False, debug=False)
else:
solution = constraint_satisfaction(stream_pddl, stream_map, INIT, terms, stream_info=stream_info,
costs=not args.unit, success_cost=success_cost,
max_time=args.max_time, search_sample_ratio=1,
debug=False)
#raise ValueError(args.algorithm)
dump_assignment(solution)
pr.disable()
pstats.Stats(pr).sort_stats('tottime').print_stats(10)
bindings, cost, evaluations = solution
if bindings is None:
return
plan = []
for name, args in SKELETON:
new_args = [bindings[a] if is_parameter(a) else a for a in args]
plan.append((name, new_args))
display_plan(tamp_problem, plan)
def main(success_cost=INF, use_costs=True): # 0 | INF
parser = argparse.ArgumentParser()
parser.add_argument('-d',
'--deterministic',
action='store_true',
help='Uses a deterministic sampler')
parser.add_argument('-a',
'--algorithm',
default='',
help='Specifies the algorithm')
parser.add_argument('-g',
'--gurobi',
action='store_true',
help='Uses gurobi')
parser.add_argument('-t',
'--max_time',
default=30,
type=int,
help='The max time')
parser.add_argument('-u',
'--unit',
action='store_true',
help='Uses unit costs')
args = parser.parse_args()
print('Arguments:', args)
np.set_printoptions(precision=2)
if args.deterministic:
set_deterministic()
print('Random seed:', get_random_seed())
tamp_problem = tight(n_robots=1, n_blocks=2, n_goals=2)
print(tamp_problem)
pddlstream_problem = pddlstream_from_tamp(tamp_problem,
use_stream=not args.gurobi,
use_optimizer=args.gurobi)
_, _, stream_pddl, stream_map, _, _ = pddlstream_problem
stream_info = {
't-region': StreamInfo(eager=True, p_success=0), # bound_fn is None
#'t-cfree': StreamInfo(eager=False, negate=True),
#'distance': FunctionInfo(opt_fn=lambda q1, q2: MOVE_COST), # Doesn't make a difference
}
terms = CONSTRAINTS
print('Constraints:', CONSTRAINTS)
if use_costs:
print('Objectives:', OBJECTIVES)
terms += OBJECTIVES
satisfaction_problem = SatisfactionProblem(stream_pddl, stream_map, INIT,
terms)
with Profiler():
if args.algorithm == 'focused':
solution = solve_pddlstream_satisfaction(
satisfaction_problem,
incremental=False,
stream_info=stream_info,
#search_sample_ratio=1,
#max_skeletons=1,
success_cost=success_cost,
max_time=args.max_time)
elif args.algorithm == 'incremental':
assert not args.gurobi
solution = solve_pddlstream_satisfaction(satisfaction_problem,
incremental=True,
success_cost=success_cost,
max_time=args.max_time,
verbose=False,
debug=False)
else:
solution = constraint_satisfaction(satisfaction_problem,
stream_info=stream_info,
costs=not args.unit,
success_cost=success_cost,
max_time=args.max_time,
search_sample_ratio=1,
debug=False)
#raise ValueError(args.algorithm)
dump_assignment(solution)
bindings, cost, evaluations = solution
if bindings is None:
return
plan = []
for name, args in SKELETON:
new_args = [bindings[a] if is_parameter(a) else a for a in args]
plan.append((name, new_args))
display_plan(tamp_problem, retime_plan(plan))
def main(success_cost=0, max_time=30):
parser = argparse.ArgumentParser()
parser.add_argument('-d',
'--deterministic',
action='store_true',
help='Uses a deterministic sampler')
parser.add_argument('-a',
'--algorithm',
default='focused',
help='Specifies the algorithm')
parser.add_argument('-o',
'--optimizer',
action='store_true',
help='Uses the optimizers')
args = parser.parse_args()
print('Arguments:', args)
np.set_printoptions(precision=2)
if args.deterministic:
seed = 0
np.random.seed(seed)
print('Random seed:', get_random_seed())
tamp_problem = get_tight_problem(n_blocks=2, n_goals=2)
print(tamp_problem)
pddlstream_problem = pddlstream_from_tamp(tamp_problem,
use_stream=not args.optimizer,
use_optimizer=args.optimizer)
stream_pddl, stream_map = pddlstream_problem[2:4]
stream_info = {
't-region': StreamInfo(eager=True, p_success=0), # bound_fn is None
#'t-cfree': StreamInfo(eager=False, negate=True),
}
terms = CONSTRAINTS + OBJECTIVES
pr = cProfile.Profile()
pr.enable()
if args.algorithm == 'focused':
solution = solve_pddlstream_satisfaction(
stream_pddl,
stream_map,
INIT,
terms,
incremental=False,
stream_info=stream_info,
#search_sample_ratio=1,
#max_iterations=1,
success_cost=success_cost,
max_time=max_time)
elif args.algorithm == 'incremental':
solution = solve_pddlstream_satisfaction(stream_pddl,
stream_map,
INIT,
terms,
incremental=True,
success_cost=success_cost,
max_time=max_time,
verbose=False)
else:
raise ValueError(args.algorithm)
dump_assignment(solution)
pr.disable()
pstats.Stats(pr).sort_stats('tottime').print_stats(10)