import random import numpy as np import copy import time from OptimalBTExpansionAlgorithm 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 modify_set(s, obj): return {f"{num}_{obj}" for num in s} def get_act_start_goal(seed=1, max_copy_times=5,cond_pred=10, depth=10, act_pred=10, total_count=1000,obj_num=0): max_copy_times=max_copy_times literals_num_set = {i for i in range(cond_pred)} all_obj_set = {i for i in range(obj_num)} act_list = [] start_list = [] goal_list = [] total_literals_count=[] total_states_num=[] total_action_num = [] total_act_pred_num=[] start_time_0=time.time() for count in range(total_count): actions_ls = [] literals_obj_set=set() state_num = 0 action_num = 1 # 生成一个规划问题，包括随机的状态和行动，以及目标状态 states = [] actions = [] start = generate_random_state(cond_pred) state = copy.deepcopy(start) states.append(state) obj_index = random.randint(0, obj_num-1) # 确定一个物体 aa_main = [] # 主路径上的动作谓词 for i in range(0, depth): a = Action() a.generate_from_state_local(state, literals_num_set,all_obj_set,obj_num,obj_index) a.cost = random.randint(0, 100) if not a in actions: a.name = "a" + str(action_num) action_num += 1 actions.append(a) aa_main.append(a) state = state_transition(state, a) # 存入一个状态 if state in states: pass else: states.append(state) state_num+= a.vaild_num goal = states[-1] # state = copy.deepcopy(start) for i in range (0,act_pred): a = Action() state = generate_random_state(cond_pred) # 随机选择一个状态 a.generate_from_state_local(state, literals_num_set, all_obj_set, obj_num) a.cost = random.randint(0, 100) if not a in actions: a.name = "a"+str(action_num) action_num+=1 actions.append(a) # 存入一个状态 if state in states: pass else: states.append(state) state_num += a.vaild_num state = state_transition(state,a) # 存入一个状态 if state in states: pass else: states.append(state) state_num += a.vaild_num for act in actions: # if act.vild_args==set(): # print(a.name,a.pre,a.add,a.cost,a.vaild_num) for obj in act.vild_args: a = Action( name=act.name + "_" + str(obj), pre=modify_set(act.pre, obj), add=modify_set(act.add, obj), del_set=modify_set(act.del_set, obj), cost=act.cost, ) if a.pre != set(): literals_obj_set |= a.pre if a.add != set(): literals_obj_set |= a.add if a.del_set != set(): literals_obj_set |= a.del_set actions_ls.append(a) # actions_ls = [ # copy.deepcopy(act).update( # name=act.name + "_" + str(obj), # pre=modify_set(act.pre, obj), # del_set=modify_set(act.del_set, obj), # add=modify_set(act.add, obj) # ) # for act in actions # for obj in act.vild_args # ] if max_copy_times != 0: for a in aa_main: # if a.vild_args == set(): # print(a.name, a.pre, a.add, a.cost, a.vaild_num) copy_times = random.randint(0, max_copy_times) for ck in range(copy_times): ca=copy.deepcopy(a) ca.cost = random.randint(1, 100) ca = ca.update( name=ca.name + "_" + str(obj_index), pre=modify_set(ca.pre, obj_index), del_set=modify_set(ca.del_set, obj_index), add=modify_set(ca.add, obj_index) ) if ca.pre != set(): literals_obj_set |= ca.pre if ca.add != set(): literals_obj_set |= ca.add if ca.del_set != set(): literals_obj_set |= ca.del_set actions_ls.append(ca) # 计算所有 文字 start = modify_set(start, obj_index) goal = modify_set(goal,obj_index) act_list.append(actions_ls) start_list.append(start) goal_list.append(goal) # print(literals_obj_set) # print(len(literals_obj_set)) total_literals_count.append(len(literals_obj_set)) total_action_num.append(len(actions_ls)) total_act_pred_num.append(len(actions)) total_states_num.append(state_num) 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 literals_obj", round(np.mean(total_literals_count), 3)) # 1000次问题的平均行动数 print("Average Number of States", round(np.mean(total_states_num), 3)) # 1000次问题的平均行动数 print("Average Number of Actions", round(np.mean(total_action_num),3)) # 1000次问题的平均行动数 print("Average Number of Act Pred", round(np.mean(total_act_pred_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_literals_count), 3),\ round(np.mean(total_states_num),3),round(np.mean(total_action_num),3),round(np.mean(total_act_pred_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_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] # obj_num_ls = [10,50,100,500] # obj_num_ls = 3*[100] # obj_num_ls = 4*[100] # obj_num_ls=3*[100] # obj_num_ls=[50] # 随机动作数量 # iters_ls= 3*[100] # iters_ls = 3* [10] # iters_ls = [100,500,1000] # iters_ls = 4*[100] # depth_ls=[30,30,10] + 3*[30] +[50,50] # obj_num_ls = [10,100] + 2*[50] + 4*[100] # iters_ls=5*[100] + 3*[500] # literals_num_ls= 7*[100] + [300] # max_copy_times_ls=[0,0] + 6*[5] # depth_ls=[30,30,10] + 3*[30] +[50] # obj_num_ls = [10,100] + 2*[50] + 3*[100] # iters_ls=5*[100] + 2*[500] # literals_num_ls= 7*[100] # max_copy_times_ls=[0,0] + 5*[5] # depth_ls=[50] # obj_num_ls = [100] # iters_ls=[500] # literals_num_ls= [300] # max_copy_times_ls=[5] # obj_num_ls = 9*[100,300,500] #9* [100,300,500] # # Iterations Action Predicates # act_pred_ls= 3*[0] + 3*[20] + 3*[40] # act_pred_ls = 3*act_pred_ls # #Condition Predicates # cond_pred_ls= 9* [10] + 9* [30] + 9*[50] # obj_num_ls = [10] #9* [100,300,500] # obj_num_ls = [100,100,500,\ # 100,100,100,100,100,300,500] # Iterations Action Predicates # cond_pred_ls = [10,10,50,\ # 10,30,50,\ # 50,50,50,50] # act_pred_ls= [10] # act_pred_ls = 3*act_pred_ls #Condition Predicates # act_pred_ls= [0,40,40,\ # 0,0,0,\ # 20,40,40,40] # # # max_copy_times_ls= [0,0] + 6*[5] #[5]*27 # max_copy_times_ls = [0]*3 + [5]*7 # obj_num_ls = [300] # # Iterations Action Predicates # act_pred_ls= [40] # #Condition Predicates # cond_pred_ls= [50] # max_copy_times_ls= 27*[0] # obj_num_ls = [100]*5+[10,300,500] # cond_pred_ls=[10,30]+[50]*5 # act_pred_ls=[0]*3+[20]+[40]*3 # max_copy_times_ls=[5]*7 # # depth_ls=[10]*27 obj_num_ls = [300] cond_pred_ls=[50] act_pred_ls=[40] max_copy_times_ls=[5]*7 depth_ls=[10]*27 all_result=[] for cond_pred,depth,act_pred,max_copy_times,obj_num in zip(cond_pred_ls, depth_ls, act_pred_ls,max_copy_times_ls,obj_num_ls): # if obj_num!=500: # continue print( f"\n------depth:{depth},obj: {obj_num},cond_pred:{cond_pred},act_pred:{act_pred},max_copy_times:{max_copy_times}-----------") # print(f"\n------literals_num: {literals_num},depth:{depth},iters:{iters},obj_num:{obj_num},max_copy_times:{max_copy_times}-----------") # 为 act建立 add映射 act_list, start_list, goal_list,literals_obj_count,state_avg,act_avg,act_pred_num = get_act_start_goal(seed=seed, max_copy_times=max_copy_times,\ cond_pred=cond_pred, depth=depth, act_pred=act_pred,\ total_count=1000,obj_num=obj_num) # param_ls = [max_copy_times,depth, obj_num,iters,act_pred_num,literals_num,act_avg] param_ls = [depth,obj_num, cond_pred, act_pred_num, max_copy_times, literals_obj_count, state_avg,act_avg] literals_num = cond_pred 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) 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) 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', # 'copy_act', # 'depth', 'obj_num','iters','act_pred_num','literals_num','act_avg', 'depth','obj_num', 'cond_pred', 'act_pred', 'max_copy_times', 'literals_obj_count', 'state_avg','act_avg', 'btalgorithm', 'tree_size_avg', 'tree_size_std', 'ticks_avg', 'ticks_std', 'wm_cond_tick_avg','wm_cond_tick_std', 'cond_tick_avg','cond_tick_std', 'cost_avg', 'cost_std', 'step_avg','step_std', # 'state_num_avg','state_num_std', 'expand_num_avg','expand_num_std', 'for_num_avg','for_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 = f'bt_randon_o={obj_num}_cp={cond_pred}_ap={act_pred+10}_MAE={max_copy_times}_time={time_str}.csv' # param_ls = [depth, obj_num, cond_pred, act_pred_num, max_copy_times, literals_obj_count, state_avg, act_avg] df.to_csv(csv_file_path, index=True) print("CSV文件已生成:", csv_file_path)