import copy from tabulate import tabulate import numpy as np import random from OptimalBTExpansionAlgorithm import generate_random_state,state_transition from OptimalBTExpansionAlgorithm import Action,OptBTExpAlgorithm from BTExpansionAlgorithm import BTExpAlgorithm # 调用最优行为树扩展算法 import time np.random.seed(1) random.seed(1) def print_action_data_table(goal,start,actions): data = [] for a in actions: data.append([a.name ,a.pre ,a.add ,a.del_set ,a.cost]) data.append(["Goal" ,goal ," " ,"Start" ,start]) print(tabulate(data, headers=["Name", "Pre", "Add" ,"Del" ,"Cost"], tablefmt="fancy_grid")) # grid plain simple github fancy_grid #行为树测试代码 def BTTest_old(bt_algo_opt=True,seed=1,literals_num=10,depth=10,iters=10,total_count=1000): if bt_algo_opt: print("============= OptBT Test ==============") else: print("============= XiaoCai BT Test ==============") random.seed(seed) # 设置生成规划问题集的超参数：文字数、解深度、迭代次数 literals_num=literals_num depth = depth iters= iters total_tree_size = [] total_action_num = [] total_state_num = [] total_steps_num=[] total_cost=[] total_tick=[] #fail_count=0 #danger_count=0 success_count =0 failure_count = 0 planning_time_total = 0.0 error = False # 实验1000次 for count in range (total_count): 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(literals_num) state = copy.deepcopy(start) states.append(state) #print (state) for i in range (0,depth): a = Action() a.generate_from_state(state,literals_num) 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] state = copy.deepcopy(start) for i in range (0,iters): a = Action() a.generate_from_state(state,literals_num) 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] # 选择测试本文算法btalgorithm，或对比算法weakalgorithm 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() start_time = time.time() # if count == 352 : #874: # print_action_data_table(goal, start, list(actions)) # print_action_data_table(goal, start, list(actions)) if algo.run_algorithm(start, goal, actions):#运行算法，规划后行为树为algo.bt total_tree_size.append( algo.bt.count_size()-1) # if count==352: # algo.print_solution() # algo.print_solution() # 打印行为树 else: print ("error") end_time = time.time() planning_time_total += (end_time-start_time) #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 <= 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=",np.mean(total_tree_size), "std=",np.std(total_tree_size, ddof=1))#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,planning_time_total/1000.0) print("Average Number of Ticks", np.mean(total_tick),"std=",np.std(total_tick,ddof=1)) print("Average Cost of Execution:", np.mean(total_cost),"std=",np.std(total_cost,ddof=1)) # print(total_steps_num) 第21个 if bt_algo_opt: print("============= End OptBT Test ==============") else: print("============= End XiaoCai BT Test ==============") # xiao cai # success: 1000 failure: 0 # Total Tree Size: mean= 35.303 std= 29.71336526001515 # Total Steps Num: mean= 1.898 std= 0.970844240101644 # Average number of states: 20.678 # Average number of actions 20.0 # Planning Time Total: 0.6280641555786133 0.0006280641555786133 # our start # success: 1000 failure: 0 # Total Tree Size: mean= 17.945 std= 12.841997192488865 # Total Steps Num: mean= 1.785 std= 0.8120556843187752 # Average number of states: 20.678 # Average number of actions 20.0 # Planning Time Total: 1.4748523235321045 0.0014748523235321046 # our # success: 1000 failure: 0 # Total Tree Size: mean= 48.764 std= 20.503626574406358 # Total Steps Num: mean= 1.785 std= 0.8120556843187752 # Average number of states: 20.678 # Average number of actions 20.0 # Planning Time Total: 3.3271877765655518 0.0033271877765655516 def BTTest(bt_algo_opt=True,seed=1,literals_num=10,depth=10,iters=10,total_count=1000): if bt_algo_opt: print("============= OptBT Test ==============") else: print("============= XiaoCai BT Test ==============") random.seed(seed) # 设置生成规划问题集的超参数：文字数、解深度、迭代次数 literals_num=literals_num depth = depth iters= iters total_tree_size = [] total_action_num = [] total_state_num = [] total_steps_num=[] total_cost=[] total_tick=[] #fail_count=0 #danger_count=0 success_count =0 failure_count = 0 planning_time_total = 0.0 error = False # 实验1000次 for count in range (total_count): 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(literals_num) state = copy.deepcopy(start) states.append(state) #print (state) for k in range(10): for i in range (0,depth): a = Action() a.generate_from_state(state,literals_num) 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] state = copy.deepcopy(start) for i in range (0,iters): a = Action() a.generate_from_state(state,literals_num) 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] # 选择测试本文算法btalgorithm，或对比算法weakalgorithm 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() start_time = time.time() if count == 0 : #874: print_action_data_table(goal, start, list(actions)) # print_action_data_table(goal, start, list(actions)) if algo.run_algorithm_test(start, goal, actions):#运行算法，规划后行为树为algo.bt total_tree_size.append( algo.bt.count_size()-1) # if count==0: # algo.print_solution() # algo.print_solution() # 打印行为树 else: print ("error") end_time = time.time() planning_time_total += (end_time-start_time) #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 <= 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=",np.mean(total_tree_size), "std=",np.std(total_tree_size, ddof=1))#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,planning_time_total/1000.0) print("Average Number of Ticks", np.mean(total_tick),"std=",np.std(total_tick,ddof=1)) print("Average Cost of Execution:", np.mean(total_cost),"std=",np.std(total_cost,ddof=1)) # print(total_steps_num) 第21个 if bt_algo_opt: print("============= End OptBT Test ==============") else: print("============= End XiaoCai BT Test ==============") # xiao cai # success: 1000 failure: 0 # Total Tree Size: mean= 35.303 std= 29.71336526001515 # Total Steps Num: mean= 1.898 std= 0.970844240101644 # Average number of states: 20.678 # Average number of actions 20.0 # Planning Time Total: 0.6280641555786133 0.0006280641555786133 # our start # success: 1000 failure: 0 # Total Tree Size: mean= 17.945 std= 12.841997192488865 # Total Steps Num: mean= 1.785 std= 0.8120556843187752 # Average number of states: 20.678 # Average number of actions 20.0 # Planning Time Total: 1.4748523235321045 0.0014748523235321046 # our # success: 1000 failure: 0 # Total Tree Size: mean= 48.764 std= 20.503626574406358 # Total Steps Num: mean= 1.785 std= 0.8120556843187752 # Average number of states: 20.678 # Average number of actions 20.0 # Planning Time Total: 3.3271877765655518 0.0033271877765655516 def get_act_start_goal(seed=1,literals_num=10,depth=10,iters=10,total_count=1000): act_list=[] start_list=[] goal_list=[] 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) # print (state) for k in range(int(iters/5)): state = copy.deepcopy(start) for i in range(0, depth): a = Action() a.generate_from_state(state, literals_num) 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] state = copy.deepcopy(start) for i in range(0, int(iters/5)): a = Action() a.generate_from_state(state, literals_num) 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) # print("action:",len(actions)) return act_list, start_list, goal_list def BTTest_act_start_goal(bt_algo_opt,act_list,start_list,goal_list): if bt_algo_opt: print("============= OptBT Test ==============") else: print("============= XiaoCai BT Test ==============") # 设置生成规划问题集的超参数：文字数、解深度、迭代次数 total_tree_size = [] total_action_num = [] total_state_num = [] total_steps_num=[] total_cost=[] total_tick=[] #fail_count=0 #danger_count=0 success_count =0 failure_count = 0 planning_time_total = 0.0 error = False # 实验1000次 for count, (actions, start, goal) in enumerate(zip(act_list, start_list, goal_list)): states=[] # 生成一个规划问题，包括随机的状态和行动，以及目标状态 state = copy.deepcopy(start) states.append(state) # 选择测试本文算法btalgorithm，或对比算法weakalgorithm 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() start_time = time.time() if count == 0 : #874: print_action_data_table(goal, start, list(actions)) # print_action_data_table(goal, start, list(actions)) if algo.run_algorithm_test(start, goal, actions):#运行算法，规划后行为树为algo.bt total_tree_size.append( algo.bt.count_size()-1) if count==0: algo.print_solution() # algo.print_solution() # 打印行为树 else: print ("error") end_time = time.time() planning_time_total += (end_time-start_time) #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 <= 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=",np.mean(total_tree_size), "std=",np.std(total_tree_size, ddof=1))#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,planning_time_total/1000.0) print("Average Number of Ticks", np.mean(total_tick),"std=",np.std(total_tick,ddof=1)) print("Average Cost of Execution:", np.mean(total_cost),"std=",np.std(total_cost,ddof=1)) # print(total_steps_num) 第21个 if bt_algo_opt: print("============= End OptBT Test ==============") else: print("============= End XiaoCai BT Test ==============") # xiao cai # success: 1000 failure: 0 # Total Tree Size: mean= 35.303 std= 29.71336526001515 # Total Steps Num: mean= 1.898 std= 0.970844240101644 # Average number of states: 20.678 # Average number of actions 20.0 # Planning Time Total: 0.6280641555786133 0.0006280641555786133 # our start # success: 1000 failure: 0 # Total Tree Size: mean= 17.945 std= 12.841997192488865 # Total Steps Num: mean= 1.785 std= 0.8120556843187752 # Average number of states: 20.678 # Average number of actions 20.0 # Planning Time Total: 1.4748523235321045 0.0014748523235321046 # our # success: 1000 failure: 0 # Total Tree Size: mean= 48.764 std= 20.503626574406358 # Total Steps Num: mean= 1.785 std= 0.8120556843187752 # Average number of states: 20.678 # Average number of actions 20.0 # Planning Time Total: 3.3271877765655518 0.0033271877765655516