def main():
parser = argparse.ArgumentParser()
parser.add_argument('-p', '--problem', default='mirror', help='The name of the problem to solve')
parser.add_argument('-a', '--algorithm', default='incremental', help='Specifies the algorithm')
parser.add_argument('-c', '--cfree', action='store_true', help='Disables collisions')
parser.add_argument('-d', '--deterministic', action='store_true', help='Uses a deterministic sampler')
parser.add_argument('-g', '--gurobi', action='store_true', help='Uses gurobi')
parser.add_argument('-n', '--number', default=1, type=int, help='The number of blocks')
parser.add_argument('-o', '--optimal', action='store_true', help='Runs in an anytime mode')
parser.add_argument('-s', '--skeleton', action='store_true', help='Enforces skeleton plan constraints')
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')
parser.add_argument('-v', '--visualize', action='store_true', help='Visualizes graphs')
args = parser.parse_args()
print('Arguments:', args)
np.set_printoptions(precision=2)
if args.deterministic:
random.seed(seed=0)
np.random.seed(seed=0)
print('Random seed:', get_random_seed())
problem_from_name = {fn.__name__: fn for fn in PROBLEMS}
if args.problem not in problem_from_name:
raise ValueError(args.problem)
print('Problem:', args.problem)
problem_fn = problem_from_name[args.problem]
tamp_problem = problem_fn(args.number)
print(tamp_problem)
pddlstream_problem = pddlstream_from_tamp(tamp_problem, collisions=not args.cfree,
use_stream=not args.gurobi, use_optimizer=args.gurobi)
print('Constants:', str_from_object(pddlstream_problem.constant_map))
print('Initial:', sorted_str_from_list(pddlstream_problem.init))
print('Goal:', str_from_object(pddlstream_problem.goal))
success_cost = 0 if args.optimal else INF
planner = 'max-astar'
#planner = 'ff-wastar1'
with Profiler(field='cumtime', num=20):
if args.algorithm == 'incremental':
solution = solve_incremental(pddlstream_problem,
complexity_step=1, planner=planner,
unit_costs=args.unit, success_cost=success_cost,
max_time=args.max_time, verbose=False)
else:
raise ValueError(args.algorithm)
print_solution(solution)
plan, cost, evaluations = solution
if plan is not None:
display_plan(tamp_problem, plan)
def dump_pddlstream(pddlstream_problem):
print('Initial:', sorted_str_from_list(pddlstream_problem.init))
print('Goal:', str_from_object(pddlstream_problem.goal))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-p',
'--problem',
default='blocked',
help='The name of the problem to solve')
parser.add_argument('-a',
'--algorithm',
default='focused',
help='Specifies the algorithm')
parser.add_argument('-c',
'--cfree',
action='store_true',
help='Disables collisions')
parser.add_argument('-d',
'--deterministic',
action='store_true',
help='Uses a deterministic sampler')
parser.add_argument('-g',
'--gurobi',
action='store_true',
help='Uses gurobi')
parser.add_argument('-n',
'--number',
default=2,
type=int,
help='The number of blocks')
parser.add_argument('-o',
'--optimal',
action='store_true',
help='Runs in an anytime mode')
parser.add_argument('-s',
'--skeleton',
action='store_true',
help='Enforces skeleton plan constraints')
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')
parser.add_argument('-v',
'--visualize',
action='store_true',
help='Visualizes graphs')
args = parser.parse_args()
print('Arguments:', args)
np.set_printoptions(precision=2)
if args.deterministic:
random.seed(seed=0)
np.random.seed(seed=0)
print('Random seed:', get_random_seed())
problem_from_name = {fn.__name__: fn for fn in PROBLEMS}
if args.problem not in problem_from_name:
raise ValueError(args.problem)
print('Problem:', args.problem)
problem_fn = problem_from_name[args.problem]
tamp_problem = problem_fn(args.number)
print(tamp_problem)
action_info = {
#'move': ActionInfo(terminal=True),
#'pick': ActionInfo(terminal=True),
#'place': ActionInfo(terminal=True),
}
stream_info = {
't-region': StreamInfo(eager=False, p_success=0), # bound_fn is None
't-cfree': StreamInfo(eager=False, negate=True),
'distance': FunctionInfo(opt_fn=lambda q1, q2: MOVE_COST),
'gurobi-cfree': StreamInfo(eager=False, negate=True),
#'gurobi': OptimizerInfo(p_success=0),
#'rrt': OptimizerInfo(p_success=0),
}
hierarchy = [
#ABSTRIPSLayer(pos_pre=['atconf']), #, horizon=1),
]
skeletons = [TIGHT_SKELETON] if args.skeleton else None
max_cost = INF # 8*MOVE_COST
constraints = PlanConstraints(
skeletons=skeletons,
#skeletons=[],
#skeletons=[skeleton, []],
exact=True,
max_cost=max_cost)
pddlstream_problem = pddlstream_from_tamp(tamp_problem,
collisions=not args.cfree,
use_stream=not args.gurobi,
use_optimizer=args.gurobi)
print('Initial:', sorted_str_from_list(pddlstream_problem.init))
print('Goal:', str_from_object(pddlstream_problem.goal))
pr = cProfile.Profile()
pr.enable()
success_cost = 0 if args.optimal else INF
planner = 'max-astar'
#planner = 'ff-wastar1'
if args.algorithm == 'focused':
solution = solve_focused(
pddlstream_problem,
constraints=constraints,
action_info=action_info,
stream_info=stream_info,
planner=planner,
max_planner_time=10,
hierarchy=hierarchy,
debug=False,
max_time=args.max_time,
max_iterations=INF,
verbose=True,
unit_costs=args.unit,
success_cost=success_cost,
unit_efforts=False,
effort_weight=0,
search_sample_ratio=1,
#max_skeletons=None, bind=True,
visualize=args.visualize)
elif args.algorithm == 'incremental':
solution = solve_incremental(pddlstream_problem,
constraints=constraints,
complexity_step=2,
planner=planner,
hierarchy=hierarchy,
unit_costs=args.unit,
success_cost=success_cost,
max_time=args.max_time,
verbose=False)
else:
raise ValueError(args.algorithm)
print_solution(solution)
plan, cost, evaluations = solution
pr.disable()
pstats.Stats(pr).sort_stats('cumtime').print_stats(20)
if plan is not None:
display_plan(tamp_problem, retime_plan(plan))