修PPObug

PPO/arguments.py

@@ -1,27 +0,0 @@
-"""
-	This file contains the arguments to parse at command line.
-	File main.py will call get_args, which then the arguments
-	will be returned.
-"""
-import argparse
-
-def get_args():
-	"""
-		Description:
-		Parses arguments at command line.
-
-		Parameters:
-			None
-
-		Return:
-			args - the arguments parsed
-	"""
-	parser = argparse.ArgumentParser()
-
-	parser.add_argument('--mode', dest='mode', type=str, default='train')              # can be 'train' or 'test'
-	parser.add_argument('--actor_model', dest='actor_model', type=str, default='')     # your actor model filename
-	parser.add_argument('--critic_model', dest='critic_model', type=str, default='')   # your critic model filename
-
-	args = parser.parse_args()
-
-	return args

PPO/env.py

@@ -139,7 +139,7 @@ class PartitionMazeEnv(gym.Env):
                         bs_time = self.bs_time_factor * (1 - rho) * d
 
                         self.rectangles[(i, j)] = {
-                            'center': ((h_boundaries[i] + h_boundaries[i+1]) * self.H / 2, (v_boundaries[j+1] - v_boundaries[j]) * self.W / 2),
+                            'center': ((h_boundaries[i] + h_boundaries[i+1]) * self.H / 2, (v_boundaries[j+1] + v_boundaries[j]) * self.W / 2),
                             'flight_time': flight_time,
                             'bs_time': bs_time,
                             'is_visited': False
@@ -244,9 +244,11 @@ class PartitionMazeEnv(gym.Env):
                 # 区域覆盖完毕，根据轨迹计算各车队的执行时间
                 T = max([self._compute_motorcade_time(idx)
                         for idx in range(self.num_cars)])
+                # print(T)
+                # print(self.car_traj)
+                reward += -(T - self.BASE_LINE)
                 print(T)
                 print(self.car_traj)
-                reward += -(T - self.BASE_LINE)
             elif done and self.step_count >= self.MAX_STEPS:
                 reward += -100
 
@@ -267,8 +269,8 @@ class PartitionMazeEnv(gym.Env):
             second_point = self.car_traj[idx][i + 1]
             car_time += math.dist(self.rectangles[tuple(first_point)]['center'], self.rectangles[tuple(second_point)]['center']) * \
                 self.car_time_factor
-        car_time + math.dist(self.rectangles[tuple(self.car_traj[idx][0])]['center'], [self.H, self.W])
+        car_time += math.dist(self.rectangles[tuple(self.car_traj[idx][0])]['center'], [self.H / 2, self.W / 2])
-        car_time + math.dist(self.rectangles[tuple(self.car_traj[idx][-1])]['center'], [self.H, self.W])
+        car_time += math.dist(self.rectangles[tuple(self.car_traj[idx][-1])]['center'], [self.H / 2, self.W / 2])
 
         return max(float(car_time) + flight_time, bs_time)
 

PPO/main.py

@@ -6,8 +6,8 @@
 import gymnasium as gym
 import sys
 import torch
+import argparse
 
-from arguments import get_args
 from ppo import PPO
 from network import FeedForwardNN
 from eval_policy import eval_policy
@@ -119,5 +119,11 @@ def main(args):
 		test(env=env, actor_model=args.actor_model)
 
 if __name__ == '__main__':
-	args = get_args() # Parse arguments from command line
+	parser = argparse.ArgumentParser()
+
+	parser.add_argument('--mode', dest='mode', type=str, default='train')              # can be 'train' or 'test'
+	parser.add_argument('--actor_model', dest='actor_model', type=str, default='')     # your actor model filename
+	parser.add_argument('--critic_model', dest='critic_model', type=str, default='')   # your critic model filename
+
+	args = parser.parse_args()
 	main(args)