165 lines
5.7 KiB
Python
165 lines
5.7 KiB
Python
import random
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import numpy as np
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import copy
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import time
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from OptimalBTExpansionAlgorithm_cond2act import Action,generate_random_state,state_transition
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from Examples import *
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import os
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output_path = os.path.join(os.path.dirname(__file__), "outputs")
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from tools import print_action_data_table,BTTest_act_start_goal
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# from tools import print_action_data_table,BTTest,BTTest_act_start_goal,get_act_start_goal
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def get_act_start_goal(seed=1, literals_num=10, depth=10, iters=10, total_count=1000,pre_max=10000):
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literals_num_set = {i for i in range(literals_num)}
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act_list = []
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start_list = []
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goal_list = []
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total_action_num=[]
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total_state_num=[]
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total_time_dic = {"start_to_goal": 0,
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"random_act":0}
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start_time_0=time.time()
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for count in range(total_count):
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# 生成一个规划问题，包括随机的状态和行动，以及目标状态
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action_num = 1
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states = []
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actions = []
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start = generate_random_state(literals_num)
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state_set = {i for i in range(literals_num)}
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state = copy.deepcopy(start)
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states.append(state)
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# print (state)
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for i in range(0, depth):
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a = Action()
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# a.generate_from_state_local(state, literals_num_set,pre_max)
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a.generate_from_state_local(state, literals_num_set)
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a.cost = random.randint(1, 100)
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if not a in actions:
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a.name = "a" + str(action_num)
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action_num += 1
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actions.append(a)
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state = state_transition(state, a)
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if state in states:
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pass
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else:
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states.append(state)
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# print(state)
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goal = states[-1]
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k_act_total = int(iters* (2 / 3)/depth)
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if k_act_total<1:
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k_act_total = random.randint(1, 2)
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# for k in range(int(iters/depth)):
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for k in range(k_act_total):
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depth_random = random.randint(depth, depth+10)
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for i in range(0, depth - 1):
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a = Action()
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start_time = time.time()
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# a.generate_from_state_local(state, literals_num_set,pre_max)
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a.generate_from_state_local(state, literals_num_set)
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end_time = time.time()
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total_time_dic["start_to_goal"] += end_time - start_time
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a.cost = random.randint(1, 100)
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if not a in actions:
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a.name = "a" + str(action_num)
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action_num += 1
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actions.append(a)
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state = state_transition(state, a)
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if state in states:
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pass
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else:
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states.append(state)
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if not goal <= states[-1]:
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a = Action()
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# if len(states[-1])==0:
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# a.pre=set()
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# else:
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pre_num = random.randint(0, min(pre_max,len(states[-1])))
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# a.pre = set(np.random.choice(list(states[-1]), pre_num, replace=False))
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a.pre = set(random.sample(states[-1], pre_num))
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a.add = goal - states[-1]
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def_set = state_set - goal
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# if len(def_set)==0:
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# a.del_set=set()
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# else:
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def_num = random.randint(0, len(def_set))
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# a.del_set = set(np.random.choice(list(def_set), def_num, replace=False))
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a.del_set = set(random.sample(def_set, def_num))
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a.cost = random.randint(1, 100)
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a.name = "a" + str(action_num)
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action_num += 1
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actions.append(a)
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state = copy.deepcopy(start)
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for i in range(0, int(iters* (2 / 3))):
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a = Action()
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start_time = time.time()
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# a.generate_from_state_local(state, literals_num_set,pre_max)
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a.generate_from_state_local(state, literals_num_set)
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end_time = time.time()
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total_time_dic["random_act"] += end_time - start_time
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if not a in actions:
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a.name = "a" + str(action_num)
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action_num += 1
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actions.append(a)
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state = state_transition(state, a)
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if state in states:
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pass
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else:
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states.append(state)
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state = random.sample(states, 1)[0]
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act_list.append(actions)
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start_list.append(start)
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goal_list.append(goal)
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total_action_num.append(len(actions))
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total_state_num.append(len(states))
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end_time_0=time.time()
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print("Total Time:", end_time_0-start_time_0)
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print("Total Time (start_to_goal):", total_time_dic["start_to_goal"])
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print("Total Time (random_act):",total_time_dic["random_act"])
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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_action_data_table(goal, start, list(actions))
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return act_list, start_list, goal_list
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# # 设置生成规划问题集的超参数：文字数、解深度、迭代次数
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seed = 1
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random.seed(1)
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np.random.seed(1)
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literals_num = 3
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depth = 3
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iters = 3
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# total_count = 5
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act_list, start_list, goal_list = get_act_start_goal(seed=seed, literals_num=literals_num, depth=depth, iters=iters,
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total_count=1000,pre_max=10000)
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BTTest_act_start_goal(bt_algo_opt=True, act_list=act_list, start_list=start_list, goal_list=goal_list)
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# BTTest_act_start_goal(bt_algo_opt=False, act_list=act_list, start_list=start_list, goal_list=goal_list)
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