def solve_incremental(problem, constraints=PlanConstraints(),
unit_costs=False, success_cost=INF,
max_iterations=INF, max_time=INF,
start_complexity=0, complexity_step=1, max_complexity=INF,
verbose=False, **search_args):
"""
Solves a PDDLStream problem by alternating between applying all possible streams and searching
:param problem: a PDDLStream problem
:param constraints: PlanConstraints on the set of legal solutions
:param max_time: the maximum amount of time to apply streams
:param max_iterations: the maximum amount of search iterations
:param unit_costs: use unit action costs rather than numeric costs
:param success_cost: an exclusive (strict) upper bound on plan cost to terminate
:param start_complexity: the stream complexity on the first iteration
:param complexity_step: the increase in the complexity limit after each iteration
:param max_complexity: the maximum stream complexity
:param verbose: if True, this prints the result of each stream application
:param search_args: 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
"""
# max_complexity = 0 => current
# complexity_step = INF => exhaustive
# success_cost = terminate_cost = decision_cost
evaluations, goal_expression, domain, externals = parse_problem(
problem, constraints=constraints, unit_costs=unit_costs)
store = SolutionStore(evaluations, max_time, success_cost, verbose) # TODO: include other info here?
ensure_no_fluent_streams(externals)
if UPDATE_STATISTICS:
load_stream_statistics(externals)
num_iterations = num_calls = 0
complexity_limit = start_complexity
instantiator = Instantiator(externals, evaluations)
num_calls += process_stream_queue(instantiator, store, complexity_limit, verbose=verbose)
while not store.is_terminated() and (num_iterations <= max_iterations) and (complexity_limit <= max_complexity):
num_iterations += 1
print('Iteration: {} | Complexity: {} | Calls: {} | Evaluations: {} | Solved: {} | Cost: {} | Time: {:.3f}'.format(
num_iterations, complexity_limit, num_calls, len(evaluations),
store.has_solution(), store.best_cost, store.elapsed_time()))
plan, cost = solve_finite(evaluations, goal_expression, domain,
max_cost=min(store.best_cost, constraints.max_cost), **search_args)
if is_plan(plan):
store.add_plan(plan, cost)
if not instantiator:
break
if complexity_step is None:
# TODO: option to select the next k-smallest complexities
complexity_limit = instantiator.min_complexity()
else:
complexity_limit += complexity_step
num_calls += process_stream_queue(instantiator, store, complexity_limit, verbose=verbose)
#retrace_stream_plan(store, domain, goal_expression)
#print('Final queue size: {}'.format(len(instantiator)))
if UPDATE_STATISTICS:
write_stream_statistics(externals, verbose)
return store.extract_solution()
def solve_incremental(problem,
constraints=PlanConstraints(),
unit_costs=False,
success_cost=INF,
max_iterations=INF,
max_time=INF,
max_memory=INF,
initial_complexity=0,
complexity_step=1,
max_complexity=INF,
verbose=False,
**search_kwargs):
"""
Solves a PDDLStream problem by alternating between applying all possible streams and searching
:param problem: a PDDLStream problem
:param constraints: PlanConstraints on the set of legal solutions
:param unit_costs: use unit action costs rather than numeric costs
:param success_cost: the exclusive (strict) upper bound on plan cost to successfully terminate
:param max_time: the maximum runtime
:param max_iterations: the maximum number of search iterations
:param max_memory: the maximum amount of memory
:param initial_complexity: the initial stream complexity limit
:param complexity_step: the increase in the stream complexity limit per iteration
:param max_complexity: the maximum stream complexity limit
:param verbose: if True, print 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 (INF if no plan), and evaluations is init expanded
using stream applications
"""
# max_complexity = 0 => current
# complexity_step = INF => exhaustive
# success_cost = terminate_cost = decision_cost
# TODO: warning if optimizers are present
evaluations, goal_expression, domain, externals = parse_problem(
problem, constraints=constraints, unit_costs=unit_costs)
store = SolutionStore(
evaluations, max_time, success_cost, verbose,
max_memory=max_memory) # TODO: include other info here?
if UPDATE_STATISTICS:
load_stream_statistics(externals)
static_externals = compile_fluents_as_attachments(domain, externals)
num_iterations = num_calls = 0
complexity_limit = initial_complexity
instantiator = Instantiator(static_externals, evaluations)
num_calls += process_stream_queue(instantiator,
store,
complexity_limit,
verbose=verbose)
while not store.is_terminated() and (num_iterations < max_iterations) and (
complexity_limit <= max_complexity):
num_iterations += 1
print(
'Iteration: {} | Complexity: {} | Calls: {} | Evaluations: {} | Solved: {} | Cost: {:.3f} | '
'Search Time: {:.3f} | Sample Time: {:.3f} | Time: {:.3f}'.format(
num_iterations, complexity_limit, num_calls, len(evaluations),
store.has_solution(), store.best_cost, store.search_time,
store.sample_time, store.elapsed_time()))
plan, cost = solve_finite(evaluations,
goal_expression,
domain,
max_cost=min(store.best_cost,
constraints.max_cost),
**search_kwargs)
if is_plan(plan):
store.add_plan(plan, cost)
if not instantiator:
break
if complexity_step is None:
# TODO: option to select the next k-smallest complexities
complexity_limit = instantiator.min_complexity()
else:
complexity_limit += complexity_step
num_calls += process_stream_queue(instantiator,
store,
complexity_limit,
verbose=verbose)
#retrace_stream_plan(store, domain, goal_expression)
#print('Final queue size: {}'.format(len(instantiator)))
summary = store.export_summary()
summary.update({
'iterations': num_iterations,
'complexity': complexity_limit,
})
print('Summary: {}'.format(str_from_object(
summary, ndigits=3))) # TODO: return the summary
if UPDATE_STATISTICS:
write_stream_statistics(externals, verbose)
return store.extract_solution()
def solve_focused(problem, constraints=PlanConstraints(),
stream_info={}, action_info={}, synthesizers=[],
max_time=INF, max_iterations=INF, max_skeletons=INF,
unit_costs=False, success_cost=INF,
complexity_step=1,
unit_efforts=False, max_effort=INF, effort_weight=None,
reorder=True, search_sample_ratio=0,
visualize=False, verbose=True, **search_kwargs):
"""
Solves a PDDLStream problem by first hypothesizing stream outputs and then determining whether they exist
:param problem: a PDDLStream problem
:param constraints: PlanConstraints on the set of legal solutions
:param stream_info: a dictionary from stream name to StreamInfo altering how individual streams are handled
:param action_info: a dictionary from stream name to ActionInfo for planning and execution
:param synthesizers: a list of StreamSynthesizer objects
:param max_time: the maximum amount of time to apply streams
:param max_iterations: the maximum number of search iterations
:param max_iterations: the maximum number of plan skeletons to consider
:param unit_costs: use unit action costs rather than numeric costs
:param success_cost: an exclusive (strict) upper bound on plan cost to terminate
:param unit_efforts: use unit stream efforts rather than estimated numeric efforts
:param complexity_step: the increase in the effort limit after each failure
:param max_effort: the maximum amount of effort to consider for streams
:param effort_weight: a multiplier for stream effort compared to action costs
:param reorder: if True, stream plans are reordered to minimize the expected sampling overhead
:param search_sample_ratio: the desired ratio of search time / sample time
: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: select whether to search or sample based on expected success rates
# TODO: no optimizers during search with relaxed_stream_plan
num_iterations = search_time = sample_time = eager_calls = 0
complexity_limit = float(INITIAL_COMPLEXITY)
eager_disabled = effort_weight is None # No point if no stream effort biasing
evaluations, goal_exp, domain, externals = parse_problem(
problem, stream_info=stream_info, constraints=constraints,
unit_costs=unit_costs, unit_efforts=unit_efforts)
store = SolutionStore(evaluations, max_time, success_cost, verbose)
full_action_info = get_action_info(action_info)
load_stream_statistics(externals + synthesizers)
if visualize and not has_pygraphviz():
visualize = False
print('Warning, visualize=True requires pygraphviz. Setting visualize=False')
if visualize:
reset_visualizations()
streams, functions, negative = partition_externals(externals, verbose=verbose)
eager_externals = list(filter(lambda e: e.info.eager, externals))
skeleton_queue = SkeletonQueue(store, goal_exp, domain)
disabled = set()
while (not store.is_terminated()) and (num_iterations < max_iterations):
start_time = time.time()
num_iterations += 1
eager_instantiator = Instantiator(eager_externals, evaluations) # Only update after an increase?
if eager_disabled:
push_disabled(eager_instantiator, disabled)
eager_calls += process_stream_queue(eager_instantiator, store, complexity_limit=complexity_limit, verbose=verbose)
print('\nIteration: {} | Complexity: {} | Skeletons: {} | Skeleton Queue: {} | Disabled: {} | Evaluations: {} | '
'Eager Calls: {} | Cost: {:.3f} | Search Time: {:.3f} | Sample Time: {:.3f} | Total Time: {:.3f}'.format(
num_iterations, complexity_limit, len(skeleton_queue.skeletons), len(skeleton_queue), len(disabled),
len(evaluations), eager_calls, store.best_cost, search_time, sample_time, store.elapsed_time()))
optimistic_solve_fn = get_optimistic_solve_fn(goal_exp, domain, negative,
max_cost=min(store.best_cost, constraints.max_cost),
unit_efforts=unit_efforts, max_effort=max_effort,
effort_weight=effort_weight, **search_kwargs)
if (max_skeletons is not None) and (len(skeleton_queue.skeletons) < max_skeletons):
combined_plan, cost = iterative_plan_streams(evaluations, externals, optimistic_solve_fn, complexity_limit,
unit_efforts=unit_efforts, max_effort=max_effort)
else:
combined_plan, cost = INFEASIBLE, INF
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 = replan_with_optimizers(evaluations, stream_plan, domain, externals)
stream_plan = combine_optimizers(evaluations, stream_plan)
#stream_plan = get_synthetic_stream_plan(stream_plan, # evaluations
# [s for s in synthesizers if not s.post_only])
if reorder:
stream_plan = reorder_stream_plan(stream_plan) # This may be redundant when using reorder_combined_plan
print('Stream plan ({}, {:.3f}): {}\nAction plan ({}, {:.3f}): {}'.format(
get_length(stream_plan), compute_plan_effort(stream_plan), stream_plan,
get_length(action_plan), cost, str_from_plan(action_plan)))
if is_plan(stream_plan) and visualize:
log_plans(stream_plan, action_plan, num_iterations)
create_visualizations(evaluations, stream_plan, num_iterations)
search_time += elapsed_time(start_time)
if (stream_plan is INFEASIBLE) and (not eager_instantiator) and (not skeleton_queue) and (not disabled):
break
start_time = time.time()
if not is_plan(stream_plan):
complexity_limit += complexity_step
if not eager_disabled:
reenable_disabled(evaluations, domain, disabled)
elif not stream_plan:
store.add_plan(action_plan, cost)
if max_skeletons is None:
process_stream_plan(store, domain, disabled, stream_plan)
else:
allocated_sample_time = (search_sample_ratio * search_time) - sample_time
skeleton_queue.process(stream_plan, action_plan, cost, complexity_limit, allocated_sample_time)
sample_time += elapsed_time(start_time)
write_stream_statistics(externals + synthesizers, verbose)
return store.extract_solution()