import random import numpy as np import copy import time from OptimalBTExpansionAlgorithm_cond2act import Action,generate_random_state,state_transition from Examples import * import os output_path = os.path.join(os.path.dirname(__file__), "outputs") from tools import print_action_data_table,BTTest_act_start_goal def copy_act(a,actions,action_num,state,states,copy_time): ca_num=0 for ct in range(copy_time): ca = copy.deepcopy(a) # pre\del 都为原来的子集 # pre_num = random.randint(0, len(ca.pre)) # ca.pre = set(random.sample(ca.pre, pre_num)) # # del_set_num = random.randint(0, len(ca.del_set)) # ca.del_set = set(random.sample(ca.del_set, del_set_num)) ca.cost = random.randint(1, 100) if not ca in actions: ca_num+=1 ca.name = "a" + str(action_num) action_num += 1 actions.append(ca) s = state_transition(state, a) if s in states: pass else: states.append(s) return ca_num def get_act_start_goal(seed=1, literals_num=10, depth=10, iters=10, total_count=1000,max_copy_times=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 = 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, 10) if not a in actions: a.name = "a" + str(action_num) action_num += 1 actions.append(a) # if max_copy_times!=0: # copy_times = random.randint(1, max_copy_times) # ca_num = copy_act(a,actions,action_num,state,states,copy_times) # action_num += ca_num state = state_transition(state, a) if state in states: pass else: states.append(state) goal = states[-1] # state = copy.deepcopy(start) for i in range (0,iters): a = Action() state = generate_random_state(literals_num) a.generate_from_state_local(state, literals_num_set) a.cost = random.randint(50, 100) if not a in actions: a.name = "a"+str(action_num) action_num+=1 actions.append(a) # if max_copy_times!=0: # copy_times = random.randint(1, max_copy_times) # ca_num = copy_act(a,actions,action_num,state,states,copy_times) # action_num += ca_num 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) # # 设置生成规划问题集的超参数：文字数、解深度、迭代次数 seed = 1 random.seed(1) np.random.seed(1) literals_num = 2 depth = 2 iters = 2 # literals_num_ls=[10,10,10,100,100,10,10,10,100,100] # depth_ls=[10,10,10,10,10,50,50,50,50,50] # iters_ls=[10,100,1000,10,1000,10,100,1000,10,1000] # literals_num_ls=[10,10,10,10, 100,100,100,100] # depth_ls=[10,10,10,10, 10,10,10,10] # iters_ls=[5,10,100,500, 5,10,100,500] # literals_num_ls=[10,10,10,10,100,100,100,100] #+[100,100] # depth_ls=[10,10,10,10,10,10,10,10] #+[10,10] # iters_ls=[5,10,100,500,5,10,100,500] #+[int(500/2),int(500/4)] # max_copy_times_ls=8*[5] #+[10,20] # literals_num_ls=[10,10,10,10,100,100,100,100] + [100,100] #+[100,100] # depth_ls=15 * [10] # iters_ls=[5,10,100,300,5,10,100,300,175,105] #+[int(500/2),int(500/4)] # max_copy_times_ls=8*[5] + [10,20] #+[10,20] literals_num_ls=[10] #+[100,100] depth_ls=15 * [10] iters_ls=[10] #+[int(500/2),int(500/4)] max_copy_times_ls=6*[5]+[10,20] #+[10,20] all_result=[] for literals_num,depth,iters,max_copy_times in zip(literals_num_ls, depth_ls, iters_ls,max_copy_times_ls): print(f"\n------literals_num: {literals_num},depth:{depth},iters:{iters},max_copy_times:{max_copy_times}-----------") # 为 act建立 add映射 act_list, start_list, goal_list,state_avg,act_avg = get_act_start_goal(seed=seed, literals_num=literals_num, depth=depth, iters=iters, total_count=2,max_copy_times=max_copy_times) param_ls = [max_copy_times,literals_num,depth,iters,state_avg,act_avg] baseline_result = BTTest_act_start_goal(bt_algo_opt=False, act_list=act_list, start_list=start_list, goal_list=goal_list,literals_num=literals_num) 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) a_result=[] a_result.extend(param_ls) a_result.append("OBTEA") a_result.extend(obt_result) all_result.append(a_result) a_result=[] a_result.extend(param_ls) a_result.append("Baseline") a_result.extend(baseline_result) all_result.append(a_result) import pandas as pd df = pd.DataFrame(all_result, columns=[ 'max_actcopy', 'literals_num','depth','iters','state','act', 'btalgorithm', 'tree_size_avg', 'tree_size_std', 'ticks_avg', 'ticks_std', 'cost_avg', 'cost_std', 'step_avg','step_std', 'state_num_avg','state_num_std', 'expand_num_avg','expand_num_std', 'plan_time_avg', 'plan_time_std', 'plan_time_total']) time_str = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime()).replace("-","").replace(":","") csv_file_path = 'bt_result_copyact=1-5_pre_del_time='+time_str+'.csv' df.to_csv(csv_file_path, index=True) print("CSV文件已生成:", csv_file_path)