def simultaneous_stream_plan(evaluations, goal_expression, domain, stream_results, negated, unit_costs=False, **kwargs): if negated: raise NotImplementedError(negated) for result in stream_results: if isinstance(result.external, Stream) and result.external.is_fluent(): raise NotImplementedError('Fluents are not supported') applied_streams, deferred_streams = partition_results( evaluations, stream_results, lambda r: False) opt_evaluations = apply_streams(evaluations, applied_streams) stream_domain, stream_result_from_name = add_stream_actions( domain, deferred_streams) if any(map(is_optimizer_result, stream_results)): goal_expression = augment_goal(stream_domain, goal_expression) combined_plan, _ = solve_finite(opt_evaluations, goal_expression, stream_domain, unit_costs=unit_costs, **kwargs) if combined_plan is None: return None, INF stream_plan, action_plan = partition_plan(combined_plan, stream_result_from_name) function_plan = extract_function_plan(opt_evaluations, action_plan, domain, unit_costs) cost = get_plan_cost(opt_evaluations, action_plan, domain, unit_costs) combined_plan = stream_plan + function_plan + action_plan return combined_plan, cost
def plan_streams(evaluations, goal_expression, domain, all_results, negative, effort_weight, max_effort, simultaneous=False, reachieve=True, replan_actions=set(), **kwargs): # TODO: alternatively could translate with stream actions on real opt_state and just discard them # TODO: only consider axioms that have stream conditions? #reachieve = reachieve and not using_optimizers(all_results) #for i, result in enumerate(all_results): # print(i, result, result.get_effort()) applied_results, deferred_results = partition_results( evaluations, all_results, apply_now=lambda r: not (simultaneous or r.external.info.simultaneous)) stream_domain, deferred_from_name = add_stream_actions(domain, deferred_results) if reachieve and not using_optimizers(all_results): achieved_results = {n.result for n in evaluations.values() if isinstance(n.result, Result)} init_evaluations = {e for e, n in evaluations.items() if n.result not in achieved_results} applied_results = achieved_results | set(applied_results) evaluations = init_evaluations # For clarity # TODO: could iteratively increase max_effort node_from_atom = get_achieving_streams(evaluations, applied_results, # TODO: apply to all_results? max_effort=max_effort) opt_evaluations = {evaluation_from_fact(f): n.result for f, n in node_from_atom.items()} if UNIVERSAL_TO_CONDITIONAL or using_optimizers(all_results): goal_expression = add_unsatisfiable_to_goal(stream_domain, goal_expression) temporal = isinstance(stream_domain, SimplifiedDomain) optimistic_fn = solve_optimistic_temporal if temporal else solve_optimistic_sequential instantiated, action_instances, temporal_plan, cost = optimistic_fn( domain, stream_domain, applied_results, all_results, opt_evaluations, node_from_atom, goal_expression, effort_weight, **kwargs) if action_instances is None: return FAILED, FAILED, cost action_instances, axiom_plans = recover_axioms_plans(instantiated, action_instances) # TODO: extract out the minimum set of conditional effects that are actually required #simplify_conditional_effects(instantiated.task, action_instances) stream_plan, action_instances = recover_simultaneous( applied_results, negative, deferred_from_name, action_instances) action_plan = transform_plan_args(map(pddl_from_instance, action_instances), obj_from_pddl) replan_step = min([step+1 for step, action in enumerate(action_plan) if action.name in replan_actions] or [len(action_plan)+1]) # step after action application stream_plan, opt_plan = recover_stream_plan(evaluations, stream_plan, opt_evaluations, goal_expression, stream_domain, node_from_atom, action_instances, axiom_plans, negative, replan_step) if temporal_plan is not None: # TODO: handle deferred streams assert all(isinstance(action, Action) for action in opt_plan.action_plan) opt_plan.action_plan[:] = temporal_plan return stream_plan, opt_plan, cost
def simultaneous_stream_plan(evaluations, goal_expression, domain, stream_results, negated, unit_costs=False, **kwargs): # TODO: remove this method in favor of the more general relaxed plan if negated: raise NotImplementedError(negated) for result in stream_results: if isinstance(result.external, Stream) and result.external.is_fluent(): raise NotImplementedError('Fluents are not supported') # TODO: warning check if using simultaneous_stream_plan with non-eager functions applied_streams, deferred_streams = partition_results( evaluations, stream_results, lambda r: False) opt_evaluations = apply_streams(evaluations, applied_streams) stream_domain, stream_result_from_name = add_stream_actions( domain, deferred_streams) if any(map(is_optimizer_result, stream_results)): goal_expression = add_unsatisfiable_to_goal(stream_domain, goal_expression) combined_plan, _ = solve_finite(opt_evaluations, goal_expression, stream_domain, unit_costs=unit_costs, **kwargs) if combined_plan is None: return None, INF stream_plan, action_plan = partition_plan(combined_plan, stream_result_from_name) function_plan = extract_function_plan(opt_evaluations, action_plan, domain, unit_costs) cost = get_plan_cost(opt_evaluations, action_plan, domain, unit_costs) combined_plan = stream_plan + function_plan + action_plan return combined_plan, cost
def relaxed_stream_plan(evaluations, goal_expression, domain, stream_results, negative, unit_costs, unit_efforts, effort_weight, debug=False, **kwargs): # TODO: alternatively could translate with stream actions on real opt_state and just discard them # TODO: only consider axioms that have stream conditions? applied_results, deferred_results = partition_results(evaluations, stream_results, apply_now=lambda r: not r.external.info.simultaneous) stream_domain, result_from_name = add_stream_actions(domain, deferred_results) node_from_atom = get_achieving_streams(evaluations, applied_results) opt_evaluations = apply_streams(evaluations, applied_results) # if n.effort < INF if any(map(is_optimizer_result, stream_results)): goal_expression = augment_goal(stream_domain, goal_expression) problem = get_problem(opt_evaluations, goal_expression, stream_domain, unit_costs) # begin_metric with Verbose(debug): instantiated = instantiate_task(task_from_domain_problem(stream_domain, problem)) if instantiated is None: return None, INF if (effort_weight is not None) or any(map(is_optimizer_result, applied_results)): add_stream_costs(node_from_atom, instantiated, unit_efforts, effort_weight) add_optimizer_axioms(stream_results, instantiated) with Verbose(debug): sas_task = sas_from_instantiated(instantiated) sas_task.metric = True #sas_task = sas_from_pddl(task) #action_plan, _ = serialized_solve_from_task(sas_task, debug=debug, **kwargs) action_plan, _ = abstrips_solve_from_task(sas_task, debug=debug, **kwargs) #action_plan, _ = abstrips_solve_from_task_sequential(sas_task, debug=debug, **kwargs) if action_plan is None: return None, INF applied_plan, function_plan = partition_external_plan(recover_stream_plan( evaluations, goal_expression, stream_domain, applied_results, action_plan, negative, unit_costs)) deferred_plan, action_plan = partition_plan(action_plan, result_from_name) stream_plan = applied_plan + deferred_plan + function_plan action_plan = obj_from_pddl_plan(action_plan) cost = get_plan_cost(opt_evaluations, action_plan, domain, unit_costs) combined_plan = stream_plan + action_plan return combined_plan, cost
def plan_streams(evaluations, goal_expression, domain, all_results, negative, effort_weight, max_effort, simultaneous=False, reachieve=True, **kwargs): # TODO: alternatively could translate with stream actions on real opt_state and just discard them # TODO: only consider axioms that have stream conditions? #reachieve = reachieve and not using_optimizers(all_results) applied_results, deferred_results = partition_results( evaluations, all_results, apply_now=lambda r: not (simultaneous or r.external.info.simultaneous)) stream_domain, deferred_from_name = add_stream_actions(domain, deferred_results) if reachieve and not using_optimizers(all_results): achieved_results = {n.result for n in evaluations.values() if isinstance(n.result, Result)} init_evaluations = {e for e, n in evaluations.items() if n.result not in achieved_results} applied_results = achieved_results | set(applied_results) evaluations = init_evaluations # For clarity # TODO: could iteratively increase max_effort node_from_atom = get_achieving_streams(evaluations, applied_results, # TODO: apply to all_results? max_effort=max_effort) opt_evaluations = {evaluation_from_fact(f): n.result for f, n in node_from_atom.items()} if UNIVERSAL_TO_CONDITIONAL or using_optimizers(all_results): goal_expression = add_unsatisfiable_to_goal(stream_domain, goal_expression) instantiated, action_instances, action_plan, cost = solve_optimistic_sequential( domain, stream_domain, applied_results, all_results, opt_evaluations, node_from_atom, goal_expression, effort_weight, **kwargs) if action_plan is None: return action_plan, cost axiom_plans = recover_axioms_plans(instantiated, action_instances) # TODO: extract out the minimum set of conditional effects that are actually required #simplify_conditional_effects(instantiated.task, action_instances) stream_plan, action_instances = recover_simultaneous( applied_results, negative, deferred_from_name, action_instances) stream_plan = recover_stream_plan(evaluations, stream_plan, opt_evaluations, goal_expression, stream_domain, node_from_atom, action_instances, axiom_plans, negative) combined_plan = stream_plan + action_plan return combined_plan, cost
def relaxed_stream_plan(evaluations, goal_expression, domain, all_results, negative, unit_efforts, effort_weight, max_effort, simultaneous=False, reachieve=True, unit_costs=False, debug=False, **kwargs): # TODO: alternatively could translate with stream actions on real opt_state and just discard them # TODO: only consider axioms that have stream conditions? applied_results, deferred_results = partition_results( evaluations, all_results, apply_now=lambda r: not (simultaneous or r.external.info.simultaneous)) stream_domain, result_from_name = add_stream_actions( domain, deferred_results) opt_evaluations = apply_streams(evaluations, applied_results) # if n.effort < INF if reachieve: achieved_results = { r for r in evaluations.values() if isinstance(r, Result) } init_evaluations = { e for e, r in evaluations.items() if r not in achieved_results } applied_results = achieved_results | set(applied_results) evaluations = init_evaluations # For clarity # TODO: could iteratively increase max_effort node_from_atom = get_achieving_streams(evaluations, applied_results, unit_efforts=unit_efforts, max_effort=max_effort) if using_optimizers(all_results): goal_expression = add_unsatisfiable_to_goal(stream_domain, goal_expression) problem = get_problem(opt_evaluations, goal_expression, stream_domain, unit_costs) # begin_metric with Verbose(debug): instantiated = instantiate_task( task_from_domain_problem(stream_domain, problem)) if instantiated is None: return None, INF cost_from_action = {action: action.cost for action in instantiated.actions} if (effort_weight is not None) or using_optimizers(applied_results): add_stream_efforts(node_from_atom, instantiated, effort_weight, unit_efforts=unit_efforts) add_optimizer_axioms(all_results, instantiated) action_from_name = rename_instantiated_actions(instantiated) with Verbose(debug): sas_task = sas_from_instantiated(instantiated) sas_task.metric = True # TODO: apply renaming to hierarchy as well # solve_from_task | serialized_solve_from_task | abstrips_solve_from_task | abstrips_solve_from_task_sequential action_plan, _ = solve_from_task(sas_task, debug=debug, **kwargs) if action_plan is None: return None, INF action_instances = [action_from_name[name] for name, _ in action_plan] cost = get_plan_cost(action_instances, cost_from_action, unit_costs) axiom_plans = recover_axioms_plans(instantiated, action_instances) applied_plan, function_plan = partition_external_plan( recover_stream_plan(evaluations, opt_evaluations, goal_expression, stream_domain, node_from_atom, action_instances, axiom_plans, negative, unit_costs)) #action_plan = obj_from_pddl_plan(parse_action(instance.name) for instance in action_instances) action_plan = obj_from_pddl_plan(map(pddl_from_instance, action_instances)) deferred_plan, action_plan = partition_plan(action_plan, result_from_name) stream_plan = applied_plan + deferred_plan + function_plan combined_plan = stream_plan + action_plan return combined_plan, cost
def plan_streams(evaluations, goal_expression, domain, all_results, negative, effort_weight, max_effort, simultaneous=False, reachieve=True, debug=False, **kwargs): # TODO: alternatively could translate with stream actions on real opt_state and just discard them # TODO: only consider axioms that have stream conditions? #reachieve = reachieve and not using_optimizers(all_results) applied_results, deferred_results = partition_results( evaluations, all_results, apply_now=lambda r: not (simultaneous or r.external.info.simultaneous)) stream_domain, deferred_from_name = add_stream_actions( domain, deferred_results) if reachieve and not using_optimizers(all_results): achieved_results = { n.result for n in evaluations.values() if isinstance(n.result, Result) } init_evaluations = { e for e, n in evaluations.items() if n.result not in achieved_results } applied_results = achieved_results | set(applied_results) evaluations = init_evaluations # For clarity # TODO: could iteratively increase max_effort node_from_atom = get_achieving_streams( evaluations, applied_results, # TODO: apply to all_results? max_effort=max_effort) opt_evaluations = { evaluation_from_fact(f): n.result for f, n in node_from_atom.items() } if using_optimizers(all_results): goal_expression = add_unsatisfiable_to_goal(stream_domain, goal_expression) problem = get_problem(opt_evaluations, goal_expression, stream_domain) # begin_metric with Verbose(debug): instantiated = instantiate_task( task_from_domain_problem(stream_domain, problem)) if instantiated is None: return None, INF if using_optimizers(all_results): # TODO: reachieve=False when using optimizers or should add applied facts instantiate_optimizer_axioms(instantiated, evaluations, goal_expression, domain, all_results) cost_from_action = {action: action.cost for action in instantiated.actions} add_stream_efforts(node_from_atom, instantiated, effort_weight) if using_optimizers(applied_results): add_optimizer_effects(instantiated, node_from_atom) action_from_name = rename_instantiated_actions(instantiated) with Verbose(debug): sas_task = sas_from_instantiated(instantiated) sas_task.metric = True # TODO: apply renaming to hierarchy as well # solve_from_task | serialized_solve_from_task | abstrips_solve_from_task | abstrips_solve_from_task_sequential action_plan, raw_cost = solve_from_task(sas_task, debug=debug, **kwargs) #print(raw_cost) if action_plan is None: return None, INF action_instances = [action_from_name[name] for name, _ in action_plan] simplify_conditional_effects(instantiated.task, action_instances) stream_plan, action_instances = recover_simultaneous( applied_results, negative, deferred_from_name, action_instances) cost = get_plan_cost(action_instances, cost_from_action) axiom_plans = recover_axioms_plans(instantiated, action_instances) stream_plan = recover_stream_plan(evaluations, stream_plan, opt_evaluations, goal_expression, stream_domain, node_from_atom, action_instances, axiom_plans, negative) #action_plan = obj_from_pddl_plan(parse_action(instance.name) for instance in action_instances) action_plan = obj_from_pddl_plan(map(pddl_from_instance, action_instances)) combined_plan = stream_plan + action_plan return combined_plan, cost