206 lines
7.0 KiB
Python
206 lines
7.0 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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def copy_act(a,actions,action_num,state,states,copy_time):
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ca_num=0
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for ct in range(copy_time):
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ca = copy.deepcopy(a)
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# pre\del 都为原来的子集
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# pre_num = random.randint(0, len(ca.pre))
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# ca.pre = set(random.sample(ca.pre, pre_num))
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#
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# del_set_num = random.randint(0, len(ca.del_set))
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# ca.del_set = set(random.sample(ca.del_set, del_set_num))
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ca.cost = random.randint(1, 100)
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if not ca in actions:
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ca_num+=1
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ca.name = "a" + str(action_num)
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action_num += 1
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actions.append(ca)
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s = state_transition(state, a)
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if s in states:
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pass
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else:
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states.append(s)
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return ca_num
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def get_act_start_goal(seed=1, literals_num=10, depth=10, iters=10, total_count=1000,max_copy_times=5):
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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 = copy.deepcopy(start)
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states.append(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)
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a.cost = random.randint(1, 10)
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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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# if max_copy_times!=0:
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# copy_times = random.randint(1, max_copy_times)
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# ca_num = copy_act(a,actions,action_num,state,states,copy_times)
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# action_num += ca_num
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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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goal = states[-1]
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# state = copy.deepcopy(start)
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for i in range (0,iters):
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a = Action()
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state = generate_random_state(literals_num)
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a.generate_from_state_local(state, literals_num_set)
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a.cost = random.randint(50, 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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# if max_copy_times!=0:
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# copy_times = random.randint(1, max_copy_times)
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# ca_num = copy_act(a,actions,action_num,state,states,copy_times)
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# action_num += ca_num
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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:", round(np.mean(total_state_num),3)) # 1000次问题的平均状态数
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print("Average Number of Actions", round(np.mean(total_action_num),3)) # 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,round(np.mean(total_state_num),3),round(np.mean(total_action_num),3)
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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 = 2
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depth = 2
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iters = 2
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# literals_num_ls=[10,10,10,100,100,10,10,10,100,100]
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# depth_ls=[10,10,10,10,10,50,50,50,50,50]
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# iters_ls=[10,100,1000,10,1000,10,100,1000,10,1000]
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# literals_num_ls=[10,10,10,10, 100,100,100,100]
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# depth_ls=[10,10,10,10, 10,10,10,10]
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# iters_ls=[5,10,100,500, 5,10,100,500]
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# literals_num_ls=[10,10,10,10,100,100,100,100] #+[100,100]
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# depth_ls=[10,10,10,10,10,10,10,10] #+[10,10]
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# iters_ls=[5,10,100,500,5,10,100,500] #+[int(500/2),int(500/4)]
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# max_copy_times_ls=8*[5] #+[10,20]
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# literals_num_ls=[10,10,10,10,100,100,100,100] + [100,100] #+[100,100]
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# depth_ls=15 * [10]
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# iters_ls=[5,10,100,300,5,10,100,300,175,105] #+[int(500/2),int(500/4)]
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# max_copy_times_ls=8*[5] + [10,20] #+[10,20]
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literals_num_ls=[10] #+[100,100]
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depth_ls=15 * [10]
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iters_ls=[10] #+[int(500/2),int(500/4)]
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max_copy_times_ls=6*[5]+[10,20] #+[10,20]
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all_result=[]
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for literals_num,depth,iters,max_copy_times in zip(literals_num_ls, depth_ls, iters_ls,max_copy_times_ls):
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print(f"\n------literals_num: {literals_num},depth:{depth},iters:{iters},max_copy_times:{max_copy_times}-----------")
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# 为 act建立 add映射
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act_list, start_list, goal_list,state_avg,act_avg = get_act_start_goal(seed=seed, literals_num=literals_num, depth=depth, iters=iters,
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total_count=2,max_copy_times=max_copy_times)
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param_ls = [max_copy_times,literals_num,depth,iters,state_avg,act_avg]
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baseline_result = BTTest_act_start_goal(bt_algo_opt=False, act_list=act_list, start_list=start_list,
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goal_list=goal_list,literals_num=literals_num)
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obt_result = BTTest_act_start_goal(bt_algo_opt=True, act_list=act_list, start_list=start_list, goal_list=goal_list,literals_num=literals_num)
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a_result=[]
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a_result.extend(param_ls)
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a_result.append("OBTEA")
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a_result.extend(obt_result)
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all_result.append(a_result)
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a_result=[]
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a_result.extend(param_ls)
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a_result.append("Baseline")
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a_result.extend(baseline_result)
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all_result.append(a_result)
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import pandas as pd
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df = pd.DataFrame(all_result, columns=[
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'max_actcopy',
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'literals_num','depth','iters','state','act',
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'btalgorithm',
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'tree_size_avg', 'tree_size_std',
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'ticks_avg', 'ticks_std',
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'cost_avg', 'cost_std',
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'step_avg','step_std',
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'state_num_avg','state_num_std',
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'expand_num_avg','expand_num_std',
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'plan_time_avg', 'plan_time_std', 'plan_time_total'])
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time_str = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime()).replace("-","").replace(":","")
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csv_file_path = 'bt_result_copyact=1-5_pre_del_time='+time_str+'.csv'
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df.to_csv(csv_file_path, index=True)
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print("CSV文件已生成:", csv_file_path) |