调整eval的输出
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weixin_46229132
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@ -72,11 +72,11 @@ def main():
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# Seed Everything
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env_seed = opt.seed
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torch.manual_seed(opt.seed)
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torch.cuda.manual_seed(opt.seed)
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torch.backends.cudnn.deterministic = True
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torch.backends.cudnn.benchmark = False
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print("Random Seed: {}".format(opt.seed))
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# torch.manual_seed(opt.seed)
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# torch.cuda.manual_seed(opt.seed)
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# torch.backends.cudnn.deterministic = True
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# torch.backends.cudnn.benchmark = False
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# print("Random Seed: {}".format(opt.seed))
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# Build SummaryWriter to record training curves
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if opt.write:
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@ -2,6 +2,7 @@ import torch.nn.functional as F
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import torch.nn as nn
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import argparse
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import torch
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import numpy as np
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class Actor(nn.Module):
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def __init__(self, state_dim, action_dim, net_width, maxaction):
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@ -40,14 +41,16 @@ def evaluate_policy(env, agent, turns = 3):
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for j in range(turns):
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s = env.reset()
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done = False
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action_series = []
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while not done:
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# Take deterministic actions at test time
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a = agent.select_action(s, deterministic=True)
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s_next, r, dw, tr, info = env.step(a)
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done = (dw or tr)
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action_series.append(a[0])
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total_scores += r
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s = s_next
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print(np.round(action_series, 3))
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return int(total_scores/turns)
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@ -65,7 +65,7 @@ class DQN_agent(object):
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if state[0][0] == 0:
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a = np.random.randint(0,10)
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else:
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a = np.random.randint(10,13)
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a = np.random.randint(10,14)
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else:
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if state[0][0] == 0:
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q_value = self.q_net(state)
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@ -3,6 +3,7 @@ import os
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import shutil
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import argparse
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import torch
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import numpy as np
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import sys
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from env_dis import PartitionMazeEnv
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@ -111,19 +112,20 @@ def main():
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print('EnvName:', BriefEnvName[opt.EnvIdex],
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'seed:', opt.seed, 'score:', score)
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else:
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total_steps = 1
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total_steps = 0
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while total_steps < opt.Max_train_steps:
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# Do not use opt.seed directly, or it can overfit to opt.seed
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s = env.reset(seed=env_seed)
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env_seed += 1
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done = False
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'''Interact & trian'''
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while not done:
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# e-greedy exploration
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if total_steps < opt.random_steps:
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# TODO sample取值有问题
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a = env.action_space.sample()
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if s[0] == 0:
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a = np.random.randint(0, 10)
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else:
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a = np.random.randint(10, 14)
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else:
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a = agent.select_action(s, deterministic=False)
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s_next, r, dw, tr, info = env.step(a)
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13
env.py
13
env.py
@ -39,8 +39,9 @@ class PartitionMazeEnv(gym.Env):
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##############################
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# 可能需要手动修改的超参数
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##############################
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self.CUT_NUM = 6 # 横切一半，竖切一半
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self.BASE_LINE = 12000 # 基准时间，通过greedy或者蒙特卡洛计算出来
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self.CUT_NUM = 4 # 横切一半，竖切一半
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self.BASE_LINE = 4000 # 基准时间，通过greedy或者蒙特卡洛计算出来
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self.MAX_STEPS = 200 # 迷宫走法步数上限
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self.phase = 0 # 阶段控制，0：区域划分阶段，1：迷宫初始化阶段，2：走迷宫阶段
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self.partition_step = 0 # 区域划分阶段步数，范围 0~4
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@ -65,7 +66,6 @@ class PartitionMazeEnv(gym.Env):
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self.init_maze_step = 0
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# 路径规划阶段相关变量
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self.MAX_STEPS = 50 # 迷宫走法步数上限
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self.step_count = 0
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self.rectangles = {}
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self.car_pos = [(self.H / 2, self.W / 2) for _ in range(self.num_cars)]
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@ -159,16 +159,11 @@ class PartitionMazeEnv(gym.Env):
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else:
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# 进入阶段 1：初始化迷宫
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self.phase = 1
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state = np.concatenate(
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[self.partition_values, np.array(self.car_pos).flatten()])
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reward = 10
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# 构建反向索引，方便后续计算
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self.reverse_rectangles = {v['center']: k for k, v in self.rectangles.items()}
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return state, reward, False, False, {}
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elif self.phase == 1:
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# 阶段 1：初始化迷宫，让多个车辆从区域中心出发，前往最近的几个区域中心点
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region_centers = [
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(i, j, self.rectangles[(i, j)]['center'])
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for i in range(len(self.row_cuts) - 1)
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@ -191,7 +186,7 @@ class PartitionMazeEnv(gym.Env):
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state = np.concatenate(
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[self.partition_values, np.array(self.car_pos).flatten()]
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)
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return state, 0.0, False, False, {}
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return state, reward, False, False, {}
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elif self.phase == 2:
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# 阶段 2：路径规划（走迷宫）
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18
env_dis.py
18
env_dis.py
@ -47,9 +47,9 @@ class PartitionMazeEnv(gym.Env):
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self.partition_values = np.zeros(
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self.CUT_NUM, dtype=np.float32) # 存储 c₁, c₂, r₁, r₂
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# 定义动作空间：长度为 14 的离散动作空间
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# 前 10 个表示切分动作 {0, 0.1, ..., 0.9}，后 4 个表示上下左右移动
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self.action_space = spaces.Discrete(14)
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# 定义动作空间：长度为 15 的离散动作空间
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# 前 10 个表示切分动作 {0, 0.1, ..., 0.9}，后 5 个表示上下左右移动和保持不动
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self.action_space = spaces.Discrete(15)
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# 定义观察空间为8维向量
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# TODO 返回的状态目前只有位置坐标
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@ -156,19 +156,14 @@ class PartitionMazeEnv(gym.Env):
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[[self.phase], self.partition_values, np.zeros(np.array(self.car_pos).flatten().shape[0], dtype=np.float32)])
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return state, reward, True, False, {}
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else:
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# 进入阶段 1：初始化迷宫
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# 初始化迷宫
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self.phase = 1
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state = np.concatenate(
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[[self.phase], self.partition_values, np.array(self.car_pos).flatten()])
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reward = 10
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# 构建反向索引，方便后续计算
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self.reverse_rectangles = {
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v['center']: k for k, v in self.rectangles.items()}
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return state, reward, False, False, {}
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elif self.phase == 1:
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# TODO 阶段一可以不写出来！！！
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# 阶段 1：初始化迷宫，让多个车辆从区域中心出发，前往最近的几个区域中心点
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region_centers = [
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(i, j, self.rectangles[(i, j)]['center'])
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@ -193,7 +188,7 @@ class PartitionMazeEnv(gym.Env):
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[[self.phase], self.partition_values,
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np.array(self.car_pos).flatten()]
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)
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return state, 0.0, False, False, {}
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return state, reward, False, False, {}
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elif self.phase == 2:
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# 阶段 2：路径规划（走迷宫）
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@ -212,6 +207,9 @@ class PartitionMazeEnv(gym.Env):
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new_col = current_col - 1
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elif action == 13 and current_col < len(self.col_cuts) - 2: # 右
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new_col = new_col + 1
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else:
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# 无效动作，保持原地
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pass
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# 更新车辆位置
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self.car_pos[current_car] = self.rectangles[(
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@ -1,12 +1,13 @@
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# from env import PartitionMazeEnv
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from env_dis import PartitionMazeEnv
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from env import PartitionMazeEnv
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# from env_dis import PartitionMazeEnv
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env = PartitionMazeEnv()
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state = env.reset()
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print(state)
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action_series = [0, 0, 3, 0, 0, 10]
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action_series = [[0.1], [0.2], [0.4], [0], [0.1]]
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# action_series = [0, 0, 3, 0, 0, 10]
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for i in range(100):
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action = action_series[i]
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16
params2.yml
Normal file
16
params2.yml
Normal file
@ -0,0 +1,16 @@
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H : 50 # 区域高度，网格点之间的距离为25m（单位距离）
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W : 50 # 区域宽度
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num_cars : 3 # 系统数量（车-巢-机系统个数）
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# 时间系数（单位：秒，每个网格一张照片）
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flight_time_factor : 3 # 每张照片对应的飞行时间，无人机飞行速度为9.5m/s，拍摄照片的时间间隔为3s
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comp_time_factor : 5 # 无人机上每张照片计算时间，5s
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trans_time_factor : 0.3 # 每张照片传输时间，0.3s
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car_time_factor : 100 # TODO 汽车每单位距离的移动时间，2s，加了一个放大因子50
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bs_time_factor : 5 # 机巢上每张照片计算时间
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# 其他参数
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flight_energy_factor : 0.05 # 单位：分钟/张
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comp_energy_factor : 0.05 # TODO 计算能耗需要重新估计
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trans_energy_factor : 0.0025
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battery_energy_capacity : 20 # 无人机只进行飞行，续航为30分钟
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