978 lines
39 KiB
Python
978 lines
39 KiB
Python
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from utils.bt.load import load_behavior_tree_lib
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from OptimalBTExpansionAlgorithm import Action,OptBTExpAlgorithm
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import copy
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from tabulate import tabulate
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import numpy as np
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import os
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from sympy import symbols, Not, Or, And, to_dnf
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from OptimalBTExpansionAlgorithm import Action,OptBTExpAlgorithm
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from BTExpansionAlgorithm import BTExpAlgorithm # 调用最优行为树扩展算法
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import time
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from utils.bt.draw import render_dot_tree
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from utils.bt.load import load_bt_from_ptml
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from EXP.behavior_lib._base.Behavior import Bahavior
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from EXP.behavior_lib.cond import Holding
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root_path = os.path.abspath(
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os.path.join(__file__, "../../..")
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)
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def goal_transfer_str(goal):
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goal_dnf = str(to_dnf(goal, simplify=True))
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# print(goal_dnf)
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goal_set = []
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if ('|' in goal or '&' in goal or 'Not' in goal) or not '(' in goal:
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goal_ls = goal_dnf.split("|")
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for g in goal_ls:
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g_set = set()
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g = g.replace(" ", "").replace("(", "").replace(")", "")
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g = g.split("&")
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for literal in g:
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if '_' in literal:
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first_part, rest = literal.split('_', 1)
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literal = first_part + '(' + rest
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# 添加 ')' 到末尾
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literal += ')'
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# 替换剩余的 '_' 为 ','
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literal = literal.replace('_', ',')
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literal=literal.replace('~', 'Not ')
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g_set.add(literal)
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goal_set.append(g_set)
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else:
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g_set = set()
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w = goal.split(")")
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g_set.add(w[0] + ")")
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if len(w) > 1:
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for x in w[1:]:
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if x != "":
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g_set.add(x[1:] + ")")
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goal_set.append(g_set)
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return goal_set
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def collect_action_nodes_multiple_num(random,multiple_num=1):
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behavior_dict = load_behavior_tree_lib()
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action_list = []
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for cls in behavior_dict["act"].values():
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if cls.can_be_expanded:
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print(f"可扩展动作：{cls.__name__}, 存在{len(cls.valid_args)}个有效论域组合")
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if cls.num_args == 0:
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for num in range(multiple_num):
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info = cls.get_info()
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action_list.append(Action(name=cls.get_ins_name() + str(num), **info))
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if cls.num_args == 1:
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for num in range(multiple_num):
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for arg in cls.valid_args:
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info = cls.get_info(arg)
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action_list.append(Action(name=cls.get_ins_name(arg) + str(num), **info))
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if cls.num_args > 1:
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# for num in range(multiple_num):
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for num in range(multiple_num):
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for args in cls.valid_args:
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# xx += 1
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info = cls.get_info(*args)
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action_list.append(Action(name=cls.get_ins_name(*args) + str(num),**info))
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action_list = sorted(action_list, key=lambda x: x.name)
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for i in range(len(action_list)):
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cost = random.randint(1, 100)
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action_list[i].cost=cost
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return action_list
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def collect_action_nodes(random,multiple_num=1,iters_times=1):
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behavior_dict = load_behavior_tree_lib()
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iter_action_ls=[]
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for iter in range(iters_times):
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action_list = []
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for cls in behavior_dict["act"].values():
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if cls.can_be_expanded:
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print(f"可扩展动作：{cls.__name__}, 存在{len(cls.valid_args)}个有效论域组合")
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if cls.num_args == 0:
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mr = random.randint(1, multiple_num+1)
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for num in range(mr):
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info = cls.get_info()
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action_list.append(Action(name=cls.get_ins_name() + str(num), **info))
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if cls.num_args == 1:
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mr = random.randint(1, multiple_num+1)
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for num in range(mr):
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for arg in cls.valid_args:
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info = cls.get_info(arg)
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action_list.append(Action(name=cls.get_ins_name(arg) + str(num), **info))
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if cls.num_args > 1:
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# for num in range(multiple_num):
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mr = random.randint(1, multiple_num+1)
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for num in range(mr):
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for args in cls.valid_args:
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# xx += 1
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info = cls.get_info(*args)
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action_list.append(Action(name=cls.get_ins_name(*args) + str(num),**info))
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# if xx%2==0 or xx%3==0:
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# break
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action_list = sorted(action_list, key=lambda x: x.name)
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for i in range(len(action_list)):
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cost = random.randint(1, 100)
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action_list[i].cost=cost
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iter_action_ls.append(action_list)
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print("len(action_list):",len(action_list))
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if iters_times==1:
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return action_list
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else:
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return iter_action_ls
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def collect_action_nodes_old(random):
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action_list = []
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behavior_dict = load_behavior_tree_lib()
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behavior_ls = list() # behavior_ls.sort()
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behavior_ls = [cls for cls in behavior_ls]
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behavior_ls = sorted(behavior_ls, key=lambda x: x.__class__.__name__)
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for cls in behavior_ls:
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if cls.can_be_expanded:
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print(f"可扩展动作：{cls.__name__}, 存在{len(cls.valid_args)}个有效论域组合")
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if cls.num_args == 0:
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for num in range(2):
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cost = random.randint(1, 100)
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info = cls.get_info()
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info.pop('cost', None)
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action_list.append(Action(name=cls.get_ins_name()+str(num),cost=cost, **info))
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if cls.num_args == 1:
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for num in range(2):
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for arg in cls.valid_args:
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cost = random.randint(1, 100)
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info = cls.get_info(arg)
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info.pop('cost', None)
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action_list.append(Action(name=cls.get_ins_name(arg)+str(num),cost=cost, **info))
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if cls.num_args > 1:
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for num in range(2):
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for args in cls.valid_args:
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cost = random.randint(1, 100)
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info = cls.get_info(*args)
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info.pop('cost', None)
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action_list.append(Action(name=cls.get_ins_name(*args)+str(num),cost=cost, **info))
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return action_list
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def collect_cond_nodes():
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cond_list = []
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behavior_dict = load_behavior_tree_lib()
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num=0
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vaild_num=1
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vaild_num = f"{vaild_num:.2e}"
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for cls in behavior_dict["cond"].values():
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if cls.can_be_expanded:
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print(f"可扩展条件：{cls.__name__}, 存在{len(cls.valid_args)}个有效论域组合")
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if cls.num_params == 0:
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num+=1
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# vaild_num*=2
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if cls.num_params == 1:
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num += len(cls.valid_args)
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# if cls.__name__=="Holding" or cls.__name__=='RobotNear':
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# vaild_num *= (len(cls.valid_args))
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# else:
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# vaild_num *= (2**len(cls.valid_args))
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if cls.num_params > 1:
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cartesian_product_size=1
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for s in cls.valid_args:
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cartesian_product_size *= len(s)
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num += cartesian_product_size
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# if cls.__name__ == "On":
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# vaild_num *= (len(cls.valid_args[1])**len(cls.valid_args[0]))
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return num,vaild_num
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def get_start():
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# start_robowaiter = {'At(Robot,Bar)', 'Is(AC,Off)',
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# 'Exist(Yogurt)', 'Exist(BottledDrink)', 'Exist(Softdrink)', 'Exist(ADMilk)',
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# 'On(Yogurt,Bar)','On(BottledDrink,Bar)','On(ADMilk,Bar)','On(Chips,Bar)',
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# 'Exist(Milk)', 'On(Softdrink,Table1)', 'On(Softdrink,Table3)',
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# 'Exist(Chips)', 'Exist(NFCJuice)', 'Exist(Bernachon)', 'Exist(ADMilk)', 'Exist(SpringWater)', 'Exist(MilkDrink)',
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# 'Exist(ADMilk)','On(ADMilk,Bar)','On(Bernachon,Bar)','On(SpringWater,Bar2)','On(MilkDrink,Bar)',
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# 'Holding(Nothing)',
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# 'Exist(VacuumCup)', 'On(VacuumCup,Table2)',
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# 'Is(HallLight,Off)', 'Is(TubeLight,On)', 'Is(Curtain,On)',
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# 'Is(Table1,Dirty)', 'Is(Floor,Dirty)', 'Is(Chairs,Dirty)'}
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start_robowaiter = {'RobotNear(Bar)',
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'Not Active(AC)','Not Active(HallLight)','Active(TubeLight)','Not Closed(Curtain)',
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'Not Exists(Coffee)','Not Exists(Water)','Not Exists(Dessert)',
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'Holding(Nothing)',
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'Not IsClean(Table1)', 'Not IsClean(Floor)', 'Not IsClean(Chairs)',
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'On(Softdrink,Table1)','On(VacuumCup,Table2)',
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# 'On(Yogurt,Bar)','On(BottledDrink,Bar)','On(ADMilk,Bar)','On(Chips,Bar)',
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# 'On(Softdrink,Table1)', 'On(Softdrink,Table3)',
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# 'On(ADMilk,Bar)','On(Bernachon,Bar)','On(SpringWater,Bar2)','On(MilkDrink,Bar)',
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# 'On(VacuumCup,Table2)',
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}
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all_obj_place= Bahavior.all_object | Bahavior.tables_for_placement | Bahavior.tables_for_guiding
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start_robowaiter |= {f'Not RobotNear({place})' for place in all_obj_place if place != 'Bar'}
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start_robowaiter |= {f'Not Holding({obj})' for obj in Bahavior.all_object}
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start_robowaiter |= {f'Exists({obj})' for obj in Bahavior.all_object if obj != 'Coffee' and obj != 'Water' and obj != 'Dessert'}
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# 'Softdrink' 在Table1
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start_robowaiter |= {f'Not On(Softdrink,{place})' for place in Bahavior.all_place if place!="Table1"}
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start_robowaiter |= {f'Not On(VacuumCup,{place})' for place in Bahavior.all_place if place != "Table2"}
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# 默认物品都在 Bar 上
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start_robowaiter |= {f'On({obj},Bar)' for obj in Bahavior.all_object if obj != 'Coffee' and obj != 'Water' and obj != 'Dessert' \
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and obj != 'Softdrink' and obj != 'VacuumCup' }
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for place in Bahavior.all_place:
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if place!="Bar":
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start_robowaiter |= {f'Not On({obj},{place})' for obj in Bahavior.all_object}
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# start_robowaiter |= {f'On({obj},{place})' for obj in Bahavior.all_object if
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# obj != 'Coffee' and obj != 'Water' and obj != 'Dessert'}
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# 这三样哪里都没有
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make_obj = {"Coffee",'Water','Dessert'}
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for place in Bahavior.all_place:
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start_robowaiter |= {f'Not On({obj},{place})' for obj in make_obj }
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return start_robowaiter
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def print_action_data_table(goal,start,actions):
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data = []
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for a in actions:
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data.append([a.name ,a.pre ,a.add ,a.del_set ,a.cost])
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data.append(["Goal" ,goal ," " ,"Start" ,start])
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print(tabulate(data, headers=["Name", "Pre", "Add" ,"Del" ,"Cost"], tablefmt="fancy_grid")) # grid plain simple github fancy_grid
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def state_transition(state,action):
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if not action.pre <= state:
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print ('error: action not applicable')
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return state
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new_state=(state | action.add) - action.del_set
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return new_state
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def BTTest_easy_medium_hard(bt_algo_opt,goal_states,action_list,start_robowaiter):
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if bt_algo_opt:
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print("============= OptBT Test ==============")
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else:
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print("============= XiaoCai BT Test ==============")
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total_tree_size = []
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total_action_num = []
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total_state_num = []
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total_steps_num = []
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total_cost = []
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total_tick = []
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success_count = 0
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failure_count = 0
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planning_time_total = 0.0
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planning_time_ls=[]
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states=[] ####？？？
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actions = copy.deepcopy(action_list)
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start = copy.deepcopy(start_robowaiter)
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error=False
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total_count = len(goal_states)
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total_cond_tick = []
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for count, goal_str in enumerate(goal_states):
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goal = copy.deepcopy(goal_transfer_str(goal_str))
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# goal = goal_str
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# print("count:", count, "goal:", goal)
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if bt_algo_opt:
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# if count==874:
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# algo = OptBTExpAlgorithm(verbose=False)
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# else:
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algo = OptBTExpAlgorithm(verbose=False)
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else:
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algo = BTExpAlgorithm(verbose=False)
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algo.clear()
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# algo = Weakalgorithm()
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# if count == 6 : #874:
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# print_action_data_table(goal, start, list(actions))
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# print_action_data_table(goal, start, list(actions))
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start_time = time.time()
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algo_right = algo.run_algorithm(start, goal, actions)
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end_time = time.time()
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planning_time_ls.append(end_time - start_time)
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planning_time_total += (end_time - start_time)
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# print("xxxx")
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if algo_right: # 运行算法，规划后行为树为algo.bt
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# total_tree_size.append(algo.bt.count_size() - 1)
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total_tree_size.append(algo.bfs_cal_tree_size())
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# if count==10:
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# algo.print_solution()
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# algo.print_solution() # 打印行为树
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# 画出行为树
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# if count == 2:
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# ptml_string = algo.get_ptml_many_act()
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# print(ptml_string)
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# file_name = "sub_task"
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# file_path = f'{file_name}.ptml'
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# with open(file_path, 'w') as file:
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# file.write(ptml_string)
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# ptml_path = os.path.join(root_path, 'BTExpansionCode/EXP/sub_task.ptml')
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# behavior_lib_path = os.path.join(root_path, 'BTExpansionCode/EXP/behavior_lib')
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# bt = load_bt_from_ptml(None, ptml_path, behavior_lib_path)
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# if bt_algo_opt:
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# render_dot_tree(bt.root, target_directory="", name="expanded_bt_obt", png_only=False)
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# else:
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# render_dot_tree(bt.root, target_directory="", name="expanded_bt_xiaocai", png_only=False)
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else:
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print("error")
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# 开始从初始状态运行行为树，测试
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state = start
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steps = 0
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current_cost = 0
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current_tick_time = 0
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current_cond_tick_time=0
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# val, obj, cost, tick_time = algo.bt.cost_tick(state, 0, 0) # tick行为树，obj为所运行的行动
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val, obj, cost, tick_time, cond_times = algo.bt.cost_tick_cond(state, 0, 0, 0)
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current_cond_tick_time += cond_times
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current_tick_time += tick_time
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current_cost += cost
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while val != 'success' and val != 'failure': # 运行直到行为树成功或失败
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state = state_transition(state, obj)
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# val, obj, cost, tick_time = algo.bt.cost_tick(state, 0, 0)
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val, obj, cost, tick_time, cond_times = algo.bt.cost_tick_cond(state, 0, 0, 0)
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current_cond_tick_time += cond_times
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current_cost += cost
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current_tick_time += tick_time
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if (val == 'failure'):
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print("bt fails at step", steps)
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error = True
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break
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steps += 1
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if (steps >= 500): # 至多运行500步
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break
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# 检查执行后状态满不满足，只有 goal 里有一个满足就行
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error = True
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for gg in goal:
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if gg<=state:
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error = False
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success_count += 1
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total_steps_num.append(steps)
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break
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if error:
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failure_count += 1
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# if not goal[0] <= state: # 错误解，目标条件不在执行后状态满足
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# # print ("wrong solution",steps)
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# failure_count += 1
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# error = True
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# else: # 正确解，满足目标条件
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# # print ("right solution",steps)
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# success_count += 1
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# total_steps_num.append(steps)
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if error:
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# print_action_data_table(goal, start, list(actions))
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# algo.print_solution()
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break
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algo.clear()
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total_action_num.append(len(actions))
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total_state_num.append(len(states))
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total_cost.append(current_cost)
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total_tick.append(current_tick_time)
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total_cond_tick.append(current_cond_tick_time)
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print("success:", success_count, "failure:", failure_count) # 算法成功和失败次数
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print("*** Total Tree Size: mean=", round(np.mean(total_tree_size),2), "std=", round(np.std(total_tree_size, ddof=1),2)) # 1000次测试树大小
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print("Total Steps Num: mean=", np.mean(total_steps_num), "std=", np.std(total_steps_num, ddof=1))
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print("Average Number of States:", np.mean(total_state_num)) # 1000次问题的平均状态数
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print("Average Number of Actions", np.mean(total_action_num)) # 1000次问题的平均行动数
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print("Planning Time Total:", planning_time_total)
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print("*** Planning Time mean=:", round(np.mean(planning_time_ls),3), "std=", round(np.std(planning_time_ls),3))
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print("*** Average Number of Ticks", round(np.mean(total_tick),3), "std=", round(np.std(total_tick, ddof=1),3))
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print("*** Average Cost of Execution:", round(np.mean(total_cost),3), "std=", round(np.std(total_cost, ddof=1),3))
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print("*** Cond Ticks:", round(np.mean(total_cond_tick), 3), "std=", round(np.std(total_cond_tick, ddof=1), 3))
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tree_size = [round(np.mean(total_tree_size), 3), round(np.std(total_tree_size, ddof=1), 3)]
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ticks = [round(np.mean(total_tick), 3), round(np.std(total_tick, ddof=1), 3)]
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cond_ticks = [round(np.mean(total_cond_tick), 3)]
|
||
cost = [round(np.mean(total_cost), 3), round(np.std(total_cost, ddof=1), 3)]
|
||
plan_time = [round(np.mean(planning_time_ls), 5), round(np.std(planning_time_ls), 5), round(planning_time_total, 5)]
|
||
|
||
tmp_ls=[]
|
||
tmp_ls.extend(tree_size)
|
||
tmp_ls.extend(ticks)
|
||
tmp_ls.extend(cond_ticks)
|
||
tmp_ls.extend(cost)
|
||
tmp_ls.extend(plan_time)
|
||
return tmp_ls
|
||
|
||
|
||
|
||
|
||
def BTTest_Merge_easy_medium_hard(bt_algo_opt,goal_states,action_list,start_robowaiter,merge_time=99999):
|
||
|
||
merge_time = merge_time
|
||
|
||
if bt_algo_opt:
|
||
print("============= OptBT Test ==============")
|
||
else:
|
||
print("============= XiaoCai BT Test ==============")
|
||
|
||
total_tree_size = []
|
||
total_action_num = []
|
||
total_state_num = []
|
||
total_steps_num = []
|
||
total_cost = []
|
||
total_tick = []
|
||
success_count = 0
|
||
failure_count = 0
|
||
planning_time_total = 0.0
|
||
planning_time_ls=[]
|
||
states=[] ####？？？
|
||
actions = copy.deepcopy(action_list)
|
||
start = copy.deepcopy(start_robowaiter)
|
||
|
||
total_cond_tick = []
|
||
|
||
error=False
|
||
|
||
total_count = len(goal_states)
|
||
|
||
for count, goal_str in enumerate(goal_states):
|
||
|
||
goal = copy.deepcopy(goal_transfer_str(goal_str))
|
||
# goal = goal_str
|
||
# print("count:", count, "goal:", goal)
|
||
|
||
|
||
if bt_algo_opt:
|
||
# if count==874:
|
||
# algo = OptBTExpAlgorithm(verbose=False)
|
||
# else:
|
||
algo = OptBTExpAlgorithm(verbose=False)
|
||
else:
|
||
algo = BTExpAlgorithm(verbose=False)
|
||
algo.clear()
|
||
|
||
# algo = Weakalgorithm()
|
||
|
||
# if count == 6 : #874:
|
||
# print_action_data_table(goal, start, list(actions))
|
||
# print_action_data_table(goal, start, list(actions))
|
||
|
||
start_time = time.time()
|
||
# algo_right = algo.run_algorithm(start, goal, actions)
|
||
algo_right = algo.run_algorithm(start, goal, actions, merge_time)
|
||
end_time = time.time()
|
||
planning_time_ls.append(end_time - start_time)
|
||
planning_time_total += (end_time - start_time)
|
||
|
||
if algo_right: # 运行算法，规划后行为树为algo.bt
|
||
# total_tree_size.append(algo.bt.count_size() - 1)
|
||
total_tree_size.append(algo.bfs_cal_tree_size())
|
||
# if count==10:
|
||
# algo.print_solution()
|
||
# algo.print_solution() # 打印行为树
|
||
# 画出行为树
|
||
# if count == 2:
|
||
# ptml_string = algo.get_ptml_many_act()
|
||
# # print(ptml_string)
|
||
# file_name = "sub_task"
|
||
# file_path = f'{file_name}.ptml'
|
||
# with open(file_path, 'w') as file:
|
||
# file.write(ptml_string)
|
||
# ptml_path = os.path.join(root_path, 'BTExpansionCode/EXP/sub_task.ptml')
|
||
# behavior_lib_path = os.path.join(root_path, 'BTExpansionCode/EXP/behavior_lib')
|
||
# bt = load_bt_from_ptml(None, ptml_path, behavior_lib_path)
|
||
# if bt_algo_opt:
|
||
# render_dot_tree(bt.root, target_directory="", name="expanded_bt_obt", png_only=False)
|
||
# else:
|
||
# render_dot_tree(bt.root, target_directory="", name="expanded_bt_xiaocai", png_only=False)
|
||
else:
|
||
print("error")
|
||
|
||
# 开始从初始状态运行行为树，测试
|
||
state = start
|
||
steps = 0
|
||
current_cost = 0
|
||
current_tick_time = 0
|
||
current_cond_tick_time = 0
|
||
val, obj, cost, tick_time,cond_times = algo.bt.cost_tick_cond(state, 0, 0,0) # tick行为树，obj为所运行的行动
|
||
# val, obj, cost, tick_time = algo.bt.cost_tick(state, 0, 0)
|
||
|
||
current_tick_time += tick_time
|
||
current_cost += cost
|
||
current_cond_tick_time+=cond_times
|
||
while val != 'success' and val != 'failure': # 运行直到行为树成功或失败
|
||
state = state_transition(state, obj)
|
||
val, obj, cost, tick_time,cond_times = algo.bt.cost_tick_cond(state, 0, 0,0)
|
||
# val, obj, cost, tick_time = algo.bt.cost_tick(state, 0, 0)
|
||
current_cost += cost
|
||
current_tick_time += tick_time
|
||
current_cond_tick_time += cond_times
|
||
if (val == 'failure'):
|
||
print("bt fails at step", steps)
|
||
error = True
|
||
break
|
||
steps += 1
|
||
if (steps >= 500): # 至多运行500步
|
||
break
|
||
# 检查执行后状态满不满足，只有 goal 里有一个满足就行
|
||
error = True
|
||
for gg in goal:
|
||
if gg<=state:
|
||
error = False
|
||
success_count += 1
|
||
total_steps_num.append(steps)
|
||
break
|
||
if error:
|
||
failure_count += 1
|
||
# if not goal[0] <= state: # 错误解，目标条件不在执行后状态满足
|
||
# # print ("wrong solution",steps)
|
||
# failure_count += 1
|
||
# error = True
|
||
# else: # 正确解，满足目标条件
|
||
# # print ("right solution",steps)
|
||
# success_count += 1
|
||
# total_steps_num.append(steps)
|
||
if error:
|
||
# print_action_data_table(goal, start, list(actions))
|
||
# algo.print_solution()
|
||
break
|
||
|
||
algo.clear()
|
||
total_action_num.append(len(actions))
|
||
total_state_num.append(len(states))
|
||
total_cost.append(current_cost)
|
||
total_tick.append(current_tick_time)
|
||
total_cond_tick.append(current_cond_tick_time)
|
||
|
||
print("success:", success_count, "failure:", failure_count) # 算法成功和失败次数
|
||
print("*** Total Tree Size: mean=", round(np.mean(total_tree_size),2), "std=", round(np.std(total_tree_size, ddof=1),2)) # 1000次测试树大小
|
||
print("Total Steps Num: mean=", np.mean(total_steps_num), "std=", np.std(total_steps_num, ddof=1))
|
||
print("Average Number of States:", np.mean(total_state_num)) # 1000次问题的平均状态数
|
||
print("Average Number of Actions", np.mean(total_action_num)) # 1000次问题的平均行动数
|
||
print("Planning Time Total:", planning_time_total)
|
||
print("*** Planning Time mean=:", round(np.mean(planning_time_ls),3), "std=", round(np.std(planning_time_ls),3))
|
||
print("*** Average Number of Ticks", round(np.mean(total_tick),3), "std=", round(np.std(total_tick, ddof=1),3))
|
||
print("*** Average Number of Cond Ticks", round(np.mean(total_cond_tick), 3), "std=", round(np.std(total_cond_tick, ddof=1), 3))
|
||
print("*** Average Cost of Execution:", round(np.mean(total_cost),3), "std=", round(np.std(total_cost, ddof=1),3))
|
||
|
||
tree_size = [round(np.mean(total_tree_size), 3), round(np.std(total_tree_size, ddof=1), 3)]
|
||
ticks = [round(np.mean(total_tick), 3), round(np.std(total_tick, ddof=1), 3)]
|
||
cond_ticks = [round(np.mean(total_cond_tick), 3), round(np.std(total_cond_tick, ddof=1), 3)]
|
||
cost = [round(np.mean(total_cost), 3), round(np.std(total_cost, ddof=1), 3)]
|
||
plan_time = [round(np.mean(planning_time_ls), 5), round(np.std(planning_time_ls), 5), round(planning_time_total, 5)]
|
||
|
||
tmp_ls=[]
|
||
tmp_ls.extend(tree_size)
|
||
tmp_ls.extend(ticks)
|
||
tmp_ls.extend(cond_ticks)
|
||
tmp_ls.extend(cost)
|
||
tmp_ls.extend(plan_time)
|
||
return tmp_ls
|
||
|
||
|
||
|
||
|
||
def BTTest(bt_algo_opt,goal_states,action_list,start_robowaiter):
|
||
|
||
if bt_algo_opt:
|
||
print("============= OptBT Test ==============")
|
||
else:
|
||
print("============= XiaoCai BT Test ==============")
|
||
|
||
total_tree_size = []
|
||
total_action_num = []
|
||
total_state_num = []
|
||
total_steps_num = []
|
||
total_cost = []
|
||
total_tick = []
|
||
success_count = 0
|
||
failure_count = 0
|
||
planning_time_total = 0.0
|
||
planning_time_ls=[]
|
||
states=[] ####？？？
|
||
actions = copy.deepcopy(action_list)
|
||
start = copy.deepcopy(start_robowaiter)
|
||
|
||
error=False
|
||
|
||
total_count = len(goal_states)
|
||
|
||
for count, goal_str in enumerate(goal_states):
|
||
|
||
goal = copy.deepcopy(goal_transfer_str(goal_str))
|
||
# goal = goal_str
|
||
print("count:", count, "goal:", goal)
|
||
|
||
|
||
if bt_algo_opt:
|
||
# if count==874:
|
||
# algo = OptBTExpAlgorithm(verbose=False)
|
||
# else:
|
||
algo = OptBTExpAlgorithm(verbose=True)
|
||
else:
|
||
algo = BTExpAlgorithm(verbose=False)
|
||
algo.clear()
|
||
|
||
# algo = Weakalgorithm()
|
||
|
||
# if count == 6 : #874:
|
||
# print_action_data_table(goal, start, list(actions))
|
||
# print_action_data_table(goal, start, list(actions))
|
||
|
||
start_time = time.time()
|
||
algo_right = algo.run_algorithm(start, goal, actions)
|
||
end_time = time.time()
|
||
planning_time_ls.append(end_time - start_time)
|
||
planning_time_total += (end_time - start_time)
|
||
|
||
if algo_right: # 运行算法，规划后行为树为algo.bt
|
||
# total_tree_size.append(algo.bt.count_size() - 1)
|
||
total_tree_size.append(algo.bfs_cal_tree_size())
|
||
# if count==10:
|
||
# algo.print_solution()
|
||
algo.print_solution() # 打印行为树
|
||
# 画出行为树
|
||
# if count == 2:
|
||
ptml_string = algo.get_ptml_many_act()
|
||
# # print(ptml_string)
|
||
file_name = "sub_task"
|
||
file_path = f'./EXP/{file_name}.ptml'
|
||
with open(file_path, 'w') as file:
|
||
file.write(ptml_string)
|
||
ptml_path = os.path.join(root_path, 'BTExpansionCode/EXP/sub_task.ptml')
|
||
behavior_lib_path = os.path.join(root_path, 'BTExpansionCode/EXP/behavior_lib')
|
||
bt = load_bt_from_ptml(None, ptml_path, behavior_lib_path)
|
||
if bt_algo_opt:
|
||
render_dot_tree(bt.root, target_directory="", name="expanded_bt_obt", png_only=False)
|
||
else:
|
||
render_dot_tree(bt.root, target_directory="", name="expanded_bt_xiaocai", png_only=False)
|
||
else:
|
||
print("error")
|
||
|
||
# 开始从初始状态运行行为树，测试
|
||
state = start
|
||
steps = 0
|
||
current_cost = 0
|
||
current_tick_time = 0
|
||
val, obj, cost, tick_time = algo.bt.cost_tick(state, 0, 0) # tick行为树，obj为所运行的行动
|
||
|
||
current_tick_time += tick_time
|
||
current_cost += cost
|
||
while val != 'success' and val != 'failure': # 运行直到行为树成功或失败
|
||
state = state_transition(state, obj)
|
||
val, obj, cost, tick_time = algo.bt.cost_tick(state, 0, 0)
|
||
current_cost += cost
|
||
current_tick_time += tick_time
|
||
if (val == 'failure'):
|
||
print("bt fails at step", steps)
|
||
error = True
|
||
break
|
||
steps += 1
|
||
if (steps >= 500): # 至多运行500步
|
||
break
|
||
if not goal[0] <= state: # 错误解，目标条件不在执行后状态满足
|
||
# print ("wrong solution",steps)
|
||
failure_count += 1
|
||
error = True
|
||
else: # 正确解，满足目标条件
|
||
# print ("right solution",steps)
|
||
success_count += 1
|
||
total_steps_num.append(steps)
|
||
if error:
|
||
# print_action_data_table(goal, start, list(actions))
|
||
# algo.print_solution()
|
||
break
|
||
|
||
algo.clear()
|
||
total_action_num.append(len(actions))
|
||
total_state_num.append(len(states))
|
||
total_cost.append(current_cost)
|
||
total_tick.append(current_tick_time)
|
||
|
||
print("success:", success_count, "failure:", failure_count) # 算法成功和失败次数
|
||
print("*** Total Tree Size: mean=", round(np.mean(total_tree_size),2), "std=", round(np.std(total_tree_size, ddof=1),2)) # 1000次测试树大小
|
||
print("Total Steps Num: mean=", np.mean(total_steps_num), "std=", np.std(total_steps_num, ddof=1))
|
||
print("Average Number of States:", np.mean(total_state_num)) # 1000次问题的平均状态数
|
||
print("Average Number of Actions", np.mean(total_action_num)) # 1000次问题的平均行动数
|
||
print("Planning Time Total:", planning_time_total)
|
||
print("*** Planning Time mean=:", round(np.mean(planning_time_ls),3), "std=", round(np.std(planning_time_ls),3))
|
||
print("*** Average Number of Ticks", round(np.mean(total_tick),3), "std=", round(np.std(total_tick, ddof=1),3))
|
||
print("*** Average Cost of Execution:", round(np.mean(total_cost),3), "std=", round(np.std(total_cost, ddof=1),3))
|
||
|
||
|
||
|
||
|
||
def BTTest_Merge(bt_algo_opt,goal_states,action_list,start_robowaiter,merge_time=3):
|
||
|
||
total_tree_size = []
|
||
total_action_num = []
|
||
total_state_num = []
|
||
total_steps_num = []
|
||
total_cost = []
|
||
total_tick = []
|
||
success_count = 0
|
||
failure_count = 0
|
||
planning_time_total = 0.0
|
||
planning_time_ls=[]
|
||
states=[] ####？？？
|
||
actions = copy.deepcopy(action_list)
|
||
start = copy.deepcopy(start_robowaiter)
|
||
|
||
error=False
|
||
|
||
total_count = len(goal_states)
|
||
|
||
total_time_dic={}
|
||
|
||
|
||
for count, goal_str in enumerate(goal_states):
|
||
goal = copy.deepcopy(goal_transfer_str(goal_str))
|
||
|
||
if bt_algo_opt:
|
||
algo = OptBTExpAlgorithm(verbose=False)
|
||
else:
|
||
algo = BTExpAlgorithm(verbose=False)
|
||
algo.clear()
|
||
|
||
start_time = time.time()
|
||
algo_right,time_dic = algo.run_algorithm(start, goal, actions,merge_time)
|
||
end_time = time.time()
|
||
planning_time_ls.append(end_time - start_time)
|
||
planning_time_total += (end_time - start_time)
|
||
|
||
if algo_right: # 运行算法，规划后行为树为algo.bt
|
||
# total_tree_size.append(algo.bt.count_size() - 1)
|
||
total_tree_size.append(algo.bfs_cal_tree_size())
|
||
# if count==10:
|
||
# algo.print_solution()
|
||
# algo.print_solution() # 打印行为树
|
||
# 画出行为树
|
||
# if count == 2:
|
||
# ptml_string = algo.get_ptml_many_act()
|
||
# file_name = "sub_task"
|
||
# file_path = f'./{file_name}.ptml'
|
||
# with open(file_path, 'w') as file:
|
||
# file.write(ptml_string)
|
||
# ptml_path = os.path.join(root_path, 'BTExpansionCode/EXP/sub_task.ptml')
|
||
# behavior_lib_path = os.path.join(root_path, 'BTExpansionCode/EXP/behavior_lib')
|
||
# bt = load_bt_from_ptml(None, ptml_path, behavior_lib_path)
|
||
# if bt_algo_opt:
|
||
# render_dot_tree(bt.root, target_directory="", name="expanded_bt_obt", png_only=False)
|
||
# else:
|
||
# render_dot_tree(bt.root, target_directory="", name="expanded_bt_xiaocai", png_only=False)
|
||
else:
|
||
print("error")
|
||
|
||
# 开始从初始状态运行行为树，测试
|
||
state = start
|
||
steps = 0
|
||
current_cost = 0
|
||
current_tick_time = 0
|
||
val, obj, cost, tick_time = algo.bt.cost_tick(state, 0, 0) # tick行为树，obj为所运行的行动
|
||
|
||
current_tick_time += tick_time
|
||
current_cost += cost
|
||
while val != 'success' and val != 'failure': # 运行直到行为树成功或失败
|
||
state = state_transition(state, obj)
|
||
val, obj, cost, tick_time = algo.bt.cost_tick(state, 0, 0)
|
||
current_cost += cost
|
||
current_tick_time += tick_time
|
||
if (val == 'failure'):
|
||
print("bt fails at step", steps)
|
||
error = True
|
||
break
|
||
steps += 1
|
||
if (steps >= 500): # 至多运行500步
|
||
break
|
||
if not goal[0] <= state: # 错误解，目标条件不在执行后状态满足
|
||
# print ("wrong solution",steps)
|
||
failure_count += 1
|
||
error = True
|
||
else: # 正确解，满足目标条件
|
||
# print ("right solution",steps)
|
||
success_count += 1
|
||
total_steps_num.append(steps)
|
||
if error:
|
||
print_action_data_table(goal, start, list(actions))
|
||
algo.print_solution()
|
||
break
|
||
|
||
algo.clear()
|
||
total_action_num.append(len(actions))
|
||
total_state_num.append(len(states))
|
||
total_cost.append(current_cost)
|
||
total_tick.append(current_tick_time)
|
||
|
||
# print("success:", success_count, "failure:", failure_count) # 算法成功和失败次数
|
||
# print("*** Total Tree Size: mean=", round(np.mean(total_tree_size),2), "std=", round(np.std(total_tree_size, ddof=1),2)) # 1000次测试树大小
|
||
# print("Total Steps Num: mean=", np.mean(total_steps_num), "std=", np.std(total_steps_num, ddof=1))
|
||
# print("Average Number of States:", np.mean(total_state_num)) # 1000次问题的平均状态数
|
||
# print("Average Number of Actions", np.mean(total_action_num)) # 1000次问题的平均行动数
|
||
# print("Planning Time Total:", planning_time_total)
|
||
# print("*** Planning Time mean=:", round(np.mean(planning_time_ls),3), "std=", round(np.std(planning_time_ls),3))
|
||
# print("*** Average Number of Ticks", round(np.mean(total_tick),3), "std=", round(np.std(total_tick, ddof=1),3))
|
||
# print("*** Average Cost of Execution:", round(np.mean(total_cost),3), "std=", round(np.std(total_cost, ddof=1),3))
|
||
|
||
tree_size=[round(np.mean(total_tree_size),2), round(np.std(total_tree_size, ddof=1),2)]
|
||
plan_time=[round(np.mean(planning_time_ls),3), round(np.std(planning_time_ls),3),round(planning_time_total,3)]
|
||
ticks=[round(np.mean(total_tick),3),round(np.std(total_tick, ddof=1),3)]
|
||
cost=[round(np.mean(total_cost),3),round(np.std(total_cost, ddof=1),3)]
|
||
return tree_size,plan_time,ticks,cost
|
||
|
||
|
||
def get_act_start_goal(seed=1, literals_num=10, depth=10, iters=10, total_count=1000):
|
||
|
||
max_copy_time = 5
|
||
|
||
literals_num_set = {i for i in range(literals_num)}
|
||
|
||
act_list = []
|
||
start_list = []
|
||
goal_list = []
|
||
total_action_num=[]
|
||
total_state_num=[]
|
||
|
||
total_time_dic = {"start_to_goal": 0,
|
||
"random_act":0}
|
||
start_time_0=time.time()
|
||
for count in range(total_count):
|
||
# 生成一个规划问题，包括随机的状态和行动，以及目标状态
|
||
action_num = 1
|
||
states = []
|
||
actions = []
|
||
start = generate_random_state(literals_num)
|
||
state_set = {i for i in range(literals_num)}
|
||
state = copy.deepcopy(start)
|
||
states.append(state)
|
||
|
||
# for i in range(0, depth):
|
||
# a = Action()
|
||
# a.generate_from_state_local(state, literals_num_set)
|
||
# a.cost = random.randint(1, 100)
|
||
# if not a in actions:
|
||
# a.name = "a" + str(action_num)
|
||
# action_num += 1
|
||
# actions.append(a)
|
||
# state = state_transition(state, a)
|
||
# if state in states:
|
||
# pass
|
||
# else:
|
||
# states.append(state)
|
||
# # print(state)
|
||
# goal = states[-1]
|
||
|
||
# k_act_total = int(iters*np.random.uniform()/depth)
|
||
# if k_act_total<1:
|
||
# k_act_total = random.randint(1, 5)
|
||
# for k in range(k_act_total):
|
||
for i in range(0, depth):
|
||
start_time = time.time()
|
||
a = Action()
|
||
a.generate_from_state_local(state, literals_num_set)
|
||
a.cost = random.randint(1, 100)
|
||
if not a in actions:
|
||
a.name = "a" + str(action_num)
|
||
action_num += 1
|
||
actions.append(a)
|
||
copy_times = random.randint(1, max_copy_time)
|
||
# copy_times = 0
|
||
for ct in range(copy_times):
|
||
ca = copy.deepcopy(a)
|
||
ca.cost = random.randint(1, 100)
|
||
if not ca in actions:
|
||
ca.name = "a" + str(action_num)
|
||
action_num += 1
|
||
actions.append(ca)
|
||
end_time = time.time()
|
||
total_time_dic["start_to_goal"] += end_time - start_time
|
||
state = state_transition(state, a)
|
||
if state in states:
|
||
pass
|
||
else:
|
||
states.append(state)
|
||
|
||
if not goal <= states[-1]:
|
||
a = Action()
|
||
pre_num = random.randint(0, len(states[-1]))
|
||
a.pre = set(random.sample(states[-1], pre_num))
|
||
a.add = goal - states[-1]
|
||
def_set = state_set - goal
|
||
def_num = random.randint(0, len(def_set))
|
||
a.del_set = set(random.sample(def_set, def_num))
|
||
a.cost = random.randint(1, 100)
|
||
a.name = "a" + str(action_num)
|
||
action_num += 1
|
||
actions.append(a)
|
||
|
||
# copy_times = random.randint(1, 5)
|
||
copy_times = 0
|
||
for ct in range(copy_times):
|
||
ca = copy.deepcopy(a)
|
||
ca.cost = random.randint(1, 100)
|
||
if not ca in actions:
|
||
ca.name = "a" + str(action_num)
|
||
action_num += 1
|
||
actions.append(ca)
|
||
|
||
|
||
state = copy.deepcopy(start)
|
||
last_act_total=iters+2*depth-len(actions)
|
||
if last_act_total<0:
|
||
last_act_total = 0
|
||
for i in range(last_act_total):
|
||
a = Action()
|
||
start_time = time.time()
|
||
a.generate_from_state_local(state, literals_num_set)
|
||
end_time = time.time()
|
||
total_time_dic["random_act"] += end_time - start_time
|
||
|
||
if not a in actions:
|
||
a.name = "a" + str(action_num)
|
||
action_num += 1
|
||
actions.append(a)
|
||
state = state_transition(state, a)
|
||
if state in states:
|
||
pass
|
||
else:
|
||
states.append(state)
|
||
state = random.sample(states, 1)[0]
|
||
|
||
act_list.append(actions)
|
||
start_list.append(start)
|
||
goal_list.append(goal)
|
||
|
||
total_action_num.append(len(actions))
|
||
total_state_num.append(len(states))
|
||
|
||
end_time_0=time.time()
|
||
|
||
print("Total Time:", end_time_0-start_time_0)
|
||
print("Total Time (start_to_goal):", total_time_dic["start_to_goal"])
|
||
print("Total Time (random_act):",total_time_dic["random_act"])
|
||
print("Average Number of States:", round(np.mean(total_state_num),3)) # 1000次问题的平均状态数
|
||
print("Average Number of Actions", round(np.mean(total_action_num),3)) # 1000次问题的平均行动数
|
||
# print_action_data_table(goal, start, list(actions))
|
||
return act_list, start_list, goal_list,round(np.mean(total_state_num),3),round(np.mean(total_action_num),3)
|
||
|
||
|
||
|