128 lines
4.7 KiB
Python
128 lines
4.7 KiB
Python
import numpy as np
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from EXP.exp_tools import state_transition,collect_action_nodes,get_start,BTTest,goal_transfer_str,collect_cond_nodes,BTTest_Merge,BTTest_Merge_easy_medium_hard
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import copy
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import random
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import re
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from OptimalBTExpansionAlgorithm import Action,OptBTExpAlgorithm
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seed = 1
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random.seed(seed)
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multiple_num=5
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iters_times= 10
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iter_action_ls = collect_action_nodes(random,multiple_num,iters_times)
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start_robowaiter = get_start()
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# 计算state总数
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state_num, vaild_state_num= collect_cond_nodes()
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# print("meta states num: ",state_num)
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# print("states num: ",vaild_state_num)
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# print("act num: ",len(action_list))
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goal_states = []
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easy_data_set_file = "../dataset/easy_instr_goal.txt"
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medium_data_set_file = "../dataset/medium_instr_goal.txt"
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hard_data_set_file = "../dataset/hard_instr_goal.txt"
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with open(easy_data_set_file, 'r', encoding="utf-8") as f:
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easy_data_set = f.read().strip()
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with open(medium_data_set_file, 'r', encoding="utf-8") as f:
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medium_data_set = f.read().strip()
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with open(hard_data_set_file, 'r', encoding="utf-8") as f:
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hard_data_set = f.read().strip()
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all_result=[]
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max_merge_times=20
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dataset_ls = [easy_data_set,medium_data_set,hard_data_set]
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parm_difficule_ls = ['Easy','Medium','Hard']
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# dataset_ls = [hard_data_set]
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# parm_difficule_ls = ['Hard']
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for index, dataset in enumerate(dataset_ls):
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print(f"\n----------- {parm_difficule_ls[index]} ----------\n")
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sections = re.split(r'\n\s*\n', dataset)
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outputs_list = [[] for _ in range(len(sections))]
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goal_set_ls = []
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for i, s in enumerate(sections):
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x, y = s.strip().splitlines()
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x = x.strip()
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y = y.strip().replace("Goal: ", "")
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goal_set_ls.append(y)
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goal_states = goal_set_ls
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import time
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# 针对一个difficult 跑10次
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condticks_avg_ls=[]
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merge_cond_tick_total = [[] for merge_time in range(max_merge_times)]
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for iter in range(iters_times):
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action_list = iter_action_ls[iter]
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planning_time_ls=[]
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planning_time_total=0
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for count, goal_str in enumerate(goal_states):
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goal = copy.deepcopy(goal_transfer_str(goal_str))
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algo = OptBTExpAlgorithm(verbose=False)
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algo.clear()
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algo.bt_merge=False
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start_time = time.time()
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algo_right = algo.run_algorithm(start_robowaiter, goal, action_list)
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end_time = time.time()
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planning_time_ls.append(end_time - start_time)
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planning_time_total += (end_time - start_time)
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for merge_time in range(max_merge_times):
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# 根据子树个数进行合并
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bt = algo.merge_subtree(merge_time)
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# 计算合并后的 cond tick
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# 开始从初始状态运行行为树，测试
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state = copy.deepcopy(start_robowaiter)
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steps = 0
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current_cond_tick_time = 0
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val, obj, cost, tick_time, cond_times = bt.cost_tick_cond(state, 0, 0, 0) # tick行为树，obj为所运行的行动
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current_cond_tick_time += cond_times
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while val != 'success' and val != 'failure': # 运行直到行为树成功或失败
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state = state_transition(state, obj)
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val, obj, cost, tick_time, cond_times = bt.cost_tick_cond(state, 0, 0, 0)
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current_cond_tick_time += cond_times
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if (val == 'failure'):
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print("bt fails at step", steps)
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error = True
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break
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steps += 1
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if (steps >= 500): # 至多运行500步
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break
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# 检查执行后状态满不满足，只有 goal 里有一个满足就行
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error = True
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for gg in goal:
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if gg <= state:
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error = False
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break
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if error:
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print("error")
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break
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# 结束从初始状态运行行为树，测试
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merge_cond_tick_total[merge_time].append(current_cond_tick_time)
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print("iter:", iter,"cond:",merge_cond_tick_total)
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merge_cond_tick_total_np = np.array(merge_cond_tick_total)
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merge_cond_tick_averages = np.mean(merge_cond_tick_total_np, axis=1)
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print(merge_cond_tick_averages)
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all_result.append(merge_cond_tick_averages)
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import pandas as pd
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df = pd.DataFrame(all_result)
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import time
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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 = 'merged_result_easy_medium_hard_'+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) |