def locally_optimize(evaluations, store, goal_expression, domain, functions, negative,
dynamic_streams, visualize, sampling_time=0):
action_plan = store.best_plan
if action_plan is None:
return None
print('\nPostprocessing | Cost: {} | Total Time: {:.3f}'.format(store.best_cost, store.elapsed_time()))
# TODO: postprocess current skeletons as well
task = task_from_domain_problem(domain, get_problem(evaluations, goal_expression, domain, unit_costs=False))
opt_stream_plan, opt_from_obj = recover_opt_stream_plan(evaluations, action_plan, task)
opt_stream_plan += optimistic_process_streams(evaluations_from_stream_plan(evaluations, opt_stream_plan), functions)
opt_action_plan = [(name, tuple(opt_from_obj.get(o, o) for o in args)) for name, args in action_plan]
pddl_plan = [(name, tuple(map(pddl_from_object, args))) for name, args in opt_action_plan]
stream_plan = recover_stream_plan(evaluations, goal_expression, domain,
opt_stream_plan, pddl_plan, negative, unit_costs=False)
stream_plan = get_synthetic_stream_plan(reorder_stream_plan(stream_plan), dynamic_streams)
# TODO: need to make this just streams
opt_evaluations = apply_streams(evaluations, stream_plan)
opt_cost = get_plan_cost(opt_evaluations, opt_action_plan, domain, unit_costs=False)
dump_plans(stream_plan, opt_action_plan, opt_cost)
if visualize:
log_plans(stream_plan, action_plan, None)
create_visualizations(evaluations, stream_plan, None)
store.start_time = time.time()
store.max_cost = store.best_cost
queue = SkeletonQueue(store, evaluations, goal_expression, domain)
queue.new_skeleton(stream_plan, opt_action_plan, opt_cost)
queue.greedily_process()
queue.timed_process(sampling_time)
def locally_optimize(evaluations, store, goal_expression, domain, functions,
negative, dynamic_streams):
action_plan = store.best_plan
if action_plan is None:
return None
print('\nPostprocessing') # TODO: postprocess current skeleton as well
task = task_from_domain_problem(
domain,
get_problem(evaluations, goal_expression, domain, unit_costs=False))
plan_instances = get_action_instances(
task, action_plan) + [get_goal_instance(task.goal)]
replace_derived(task, set(), plan_instances)
preimage = filter(lambda a: not a.negated,
plan_preimage(plan_instances, []))
opt_stream_plan = []
opt_from_obj = {}
for stream_result in extract_order(evaluations, preimage):
input_objects = tuple(
opt_from_obj.get(o, o)
for o in stream_result.instance.input_objects)
instance = stream_result.instance.external.get_instance(input_objects)
#assert(not instance.disabled)
instance.disabled = False
opt_results = instance.next_optimistic()
if not opt_results:
continue
opt_result = opt_results[0]
opt_stream_plan.append(opt_result)
for obj, opt in zip(stream_result.output_objects,
opt_result.output_objects):
opt_from_obj[obj] = opt
opt_stream_plan += optimistic_process_streams(
evaluations_from_stream_plan(evaluations, opt_stream_plan), functions)
opt_action_plan = [(name, tuple(opt_from_obj.get(o, o) for o in args))
for name, args in action_plan]
pddl_plan = [(name, map(pddl_from_object, args))
for name, args in opt_action_plan]
stream_plan = recover_stream_plan(evaluations,
goal_expression,
domain,
opt_stream_plan,
pddl_plan,
negative,
unit_costs=False)
stream_plan = reorder_stream_plan(stream_plan)
stream_plan = get_synthetic_stream_plan(stream_plan, dynamic_streams)
print('Stream plan: {}\n'
'Action plan: {}'.format(stream_plan, opt_action_plan))
opt_cost = 0 # TODO: compute this cost for real
store.start_time = time.time()
store.max_cost = store.best_cost
#sampling_time = 10
sampling_time = 0
queue = SkeletonQueue(evaluations, store)
queue.new_skeleton(stream_plan, opt_action_plan, opt_cost)
queue.greedily_process(sampling_time)
def solve_focused(problem,
stream_info={},
action_info={},
synthesizers=[],
max_time=INF,
max_cost=INF,
unit_costs=None,
effort_weight=None,
eager_layers=1,
visualize=False,
verbose=True,
postprocess=False,
**search_kwargs):
"""
Solves a PDDLStream problem by first hypothesizing stream outputs and then determining whether they exist
:param problem: a PDDLStream problem
:param action_info: a dictionary from stream name to ActionInfo for planning and execution
:param stream_info: a dictionary from stream name to StreamInfo altering how individual streams are handled
:param max_time: the maximum amount of time to apply streams
:param max_cost: a strict upper bound on plan cost
:param effort_weight: a multiplier for stream effort compared to action costs
:param eager_layers: the number of eager stream application layers per iteration
:param visualize: if True, it draws the constraint network and stream plan as a graphviz file
:param verbose: if True, this prints the result of each stream application
:param search_kwargs: keyword args for the search subroutine
:return: a tuple (plan, cost, evaluations) where plan is a sequence of actions
(or None), cost is the cost of the plan, and evaluations is init but expanded
using stream applications
"""
# TODO: return to just using the highest level samplers at the start
search_sampling_ratio = 1
solve_stream_plan_fn = relaxed_stream_plan if effort_weight is None else simultaneous_stream_plan
# TODO: warning check if using simultaneous_stream_plan or sequential_stream_plan with non-eager functions
num_iterations = 0
search_time = sample_time = 0
store = SolutionStore(max_time, max_cost,
verbose) # TODO: include other info here?
evaluations, goal_expression, domain, stream_name, externals = parse_problem(
problem, stream_info)
compile_to_exogenous(evaluations, domain, externals)
if unit_costs is None:
unit_costs = not has_costs(domain)
full_action_info = get_action_info(action_info)
load_stream_statistics(stream_name, externals + synthesizers)
if visualize:
clear_visualizations()
# TODO: somehow Functions became no longer eager?
eager_externals = list(
filter(lambda e: e.info.eager or type(e) == Function, externals))
streams, functions, negative = partition_externals(externals)
queue = SkeletonQueue(store, evaluations)
# TODO: decide max_sampling_time based on total search_time or likelihood estimates
# TODO: switch to searching if believe chance of search better than sampling
while not store.is_terminated():
num_iterations += 1
print(
'\nIteration: {} | Queue: {} | Evaluations: {} | Cost: {} '
'| Search Time: {:.3f} | Sample Time: {:.3f} | Total Time: {:.3f}'.
format(num_iterations, len(queue), len(evaluations),
store.best_cost, search_time, sample_time,
store.elapsed_time()))
start_time = time.time()
layered_process_stream_queue(
Instantiator(evaluations, eager_externals), evaluations, store,
eager_layers)
solve_stream_plan = lambda sr: solve_stream_plan_fn(
evaluations,
goal_expression,
domain,
sr,
negative,
max_cost=store.best_cost,
#max_cost=min(store.best_cost, max_cost),
unit_costs=unit_costs,
**search_kwargs)
#combined_plan, cost = solve_stream_plan(populate_results(evaluations, streams + functions))
combined_plan, cost = iterative_solve_stream_plan(
evaluations, streams, functions, solve_stream_plan)
if action_info:
combined_plan = reorder_combined_plan(evaluations, combined_plan,
full_action_info, domain)
print('Combined plan: {}'.format(combined_plan))
stream_plan, action_plan = separate_plan(combined_plan,
full_action_info)
stream_plan = reorder_stream_plan(
stream_plan) # TODO: is this strictly redundant?
stream_plan = get_synthetic_stream_plan(stream_plan, synthesizers)
print('Stream plan: {}\n'
'Action plan: {}'.format(stream_plan, action_plan))
search_time += elapsed_time(start_time)
start_time = time.time()
if stream_plan is None:
if not queue:
break
queue.process_until_success()
#queue.fairly_process()
else:
if visualize:
create_visualizations(evaluations, stream_plan, num_iterations)
queue.new_skeleton(stream_plan, action_plan, cost)
queue.greedily_process()
sample_time += elapsed_time(start_time)
start_time = time.time()
queue.timed_process(search_sampling_ratio * search_time - sample_time)
sample_time += elapsed_time(start_time)
if postprocess and (not unit_costs):
locally_optimize(evaluations, store, goal_expression, domain,
functions, negative, synthesizers)
write_stream_statistics(stream_name, externals + synthesizers, verbose)
return revert_solution(store.best_plan, store.best_cost, evaluations)