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run.py
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run.py
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from DQN.DQNAgent import DQNAgent
from DQN.Env import AirsimDroneEnv
import Tools.AirsimTools as airsimtools
import Tools.DQNTools as dqntools
import matplotlib.pyplot as plt
import numpy as np
import airsim
import os
import json
import sys
import argparse
import torch
import threading
import time
import signal
ROUND_DECIMALS = 2
DRONE_BOTTOM_LIMIT = 1
DRONE_POSITION_LEN = 3
TARGET_POSITION_LEN = 3
SPAWN_OBJECT_NAME = 'BP_spawn_point'
DISTANCE_SENSOR = ["front", "left", "right", "rfront", "lfront", "top", "bottom", 'lfbottom', 'rfbottom', 'lbbottom', 'rbbottom']
BASE_PTAH = '.\\runs\\train\\'
def get_distance_sensor_data(client:airsim.MultirotorClient, drone_name):
sensor_data = []
for sensor_name in DISTANCE_SENSOR:
sensor_data.append(client.getDistanceSensorData(sensor_name, drone_name).distance)
return sensor_data
def signal_handler(signum, frame):
global stop_event
global folder_path
print("\nTraining interrupted. Saving model...")
agent.save(f"{folder_path}\\model.pth")
plot_rewards_and_losses(range(1, episode + 1), eposide_reward, eposide_loss_avg, save_path=f'{folder_path}\\final_performance_plot.png')
print("Model saved. Exiting...")
stop_event.set()
sys.exit(0)
def plot_rewards_and_losses(episodes, rewards, average_losses, save_path):
fig, ax1 = plt.subplots(figsize=(12, 6))
# Plot rewards as bars
ax1.bar(episodes, rewards, color='blue', alpha=0.6, label='Rewards')
ax1.set_xlabel('Episodes')
ax1.set_ylabel('Rewards', color='blue')
ax1.tick_params(axis='y', labelcolor='blue')
# Create a second y-axis for average losses
ax2 = ax1.twinx()
ax2.plot(episodes, average_losses, color='red', label='Average Loss')
ax2.set_ylabel('Average Loss', color='red')
ax2.tick_params(axis='y', labelcolor='red')
# Add legends and grid
fig.tight_layout()
fig.legend(loc='upper left', bbox_to_anchor=(0.1,0.9))
ax1.grid(True)
# Save and show the plot
plt.savefig(save_path)
plt.show()
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="AirSim-DQN train.")
parser.add_argument('--batch_size', type=int, default=64, help='batch_size')
parser.add_argument('--episodes', type=int, default=5, help='number of training')
parser.add_argument('--decay_episode', type=int, default=500, help='set the episode where epsilon starts to decay')
parser.add_argument('--gamma', type=float, default=0.99, help='weight of future reward')
parser.add_argument('--epsilon', type=float, default=1, help='random action rate')
parser.add_argument('--epsilon_min', type=float, default=0.2, help='epsilon\'s minimum')
parser.add_argument('--decay', type=float, default=0.999, help='epsilon\'s decay rate')
parser.add_argument('--infinite_loop', type=bool, default=False, help='keep training until press the stop button')
parser.add_argument('--weight', type=str, default='', help='weight path')
parser.add_argument('--device', type=str, default='cpu', choices=['cpu', 'cuda'], help='Device to use for training (cpu or cuda)')
parser.add_argument('--object', type=str, default='BP_Grid', help='The object name in the vr environment, you can place objects in the VR environment and make sure that the objects you want to visit start with the same name.. Initial object is: BP_Grid')
args = parser.parse_args()
# to stop training and save the weight
stop_event = threading.Event()
device = torch.device(args.device if torch.cuda.is_available() else 'cpu')
user_home = os.path.expanduser('~')
settings_path = os.path.join(user_home, 'Documents', 'AirSim', 'settings.json')
with open(settings_path, 'r') as file:
data = json.load(file)
vehicle_names = []
vehicles = data.get('Vehicles', {})
for vehicle, _ in vehicles.items():
vehicle_names.append(vehicle)
if len(vehicle_names) > 0:
# get weight save folder path
folder_path = dqntools.create_directory(BASE_PTAH)
drone_name = vehicle_names[0]
client = airsim.MultirotorClient()
client.confirmConnection()
# len(get_distance_sensor_data(client, drone_name)) + DRONE_POSITION_LEN + TARGET_POSITION_LEN
state_dim = len(get_distance_sensor_data(client, drone_name)) + DRONE_POSITION_LEN + TARGET_POSITION_LEN
env = AirsimDroneEnv(dqntools.calculate_reward, state_dim, client, drone_name, DISTANCE_SENSOR)
agent = DQNAgent(state_dim=state_dim, action_dim=3, bacth_size=args.batch_size, epsilon=args.epsilon, decay_episode=args.decay_episode, gamma=args.gamma, device=device)
episodes = args.episodes
objects = client.simListSceneObjects(f'{args.object}[\w]*')
targets = airsimtools.get_targets(client, objects, ROUND_DECIMALS, DRONE_BOTTOM_LIMIT)
spwan_objects = client.simListSceneObjects(f'{SPAWN_OBJECT_NAME}[\w]*')
spawn_points = airsimtools.get_targets(client, spwan_objects, ROUND_DECIMALS, DRONE_BOTTOM_LIMIT)
print('best path:', targets)
if len(targets) > 0:
if args.weight != '':
try:
agent.load(args.weight)
except:
print(f"The path:{args.weight} is not exist, load weight fail.")
signal.signal(signal.SIGINT, signal_handler)
episode = 0
eposide_reward = []
eposide_loss_avg = []
while episode < episodes:
if stop_event.is_set(): # if stop event is set, stop training and save the weight
break
airsimtools.reset_drone_to_random_spawn_point(client, drone_name, spawn_points)
time.sleep(1)
targets = airsimtools.get_targets(client, objects, ROUND_DECIMALS, DRONE_BOTTOM_LIMIT)
state, _ = env.reset(targets[0])
done = False
rewards = 0
step_count = 0
total_loss = 0
agent.train_cnt = 0
while not done:
action = agent.act(state)
next_state, reward, done, _, info = env.step(action, targets, step_cnt=step_count, drone_name=drone_name)
agent.store_experience(state, action, reward, next_state, done)
state = next_state
targets = info['targets']
loss, curr_epsilon = agent.train(episode)
curr_epsilon = np.round(curr_epsilon, 4)
if loss >= 0:
total_loss += loss
rewards += reward # calculate total rewards
step_count += 1
if agent.train_cnt == 0:
loss_avg = 0
else:
loss_avg = np.round(total_loss.cpu().detach().numpy() / agent.train_cnt, 4)
if args.infinite_loop:
if done:
if info['overlap']:
status = (f'Episode: {episode + 1:5d}/N | Step: {step_count:3d} | Reward: {rewards:5d} | loss: {loss_avg:.4f} | epsilon: {curr_epsilon:.4f} | mission_state: fail')
else:
status = (f'Episode: {episode + 1:5d}/N | Step: {step_count:3d} | Reward: {rewards:5d} | loss: {loss_avg:.4f} | epsilon: {curr_epsilon:.4f} | mission_state: success')
else:
status = (f'Episode: {episode + 1:5d}/N | Step: {step_count:3d} | Reward: {rewards:5d} | loss: {loss_avg:.4f} | epsilon: {curr_epsilon:.4f} | mission_state: run')
else:
if done:
if info['overlap']:
status = (f'Episode: {episode + 1:5d}/{episodes} | Step: {step_count:3d} | Reward: {rewards:5d} | loss: {loss_avg:.4f} | epsilon: {curr_epsilon:.4f} | mission_state: fail')
else:
status = (f'Episode: {episode + 1:5d}/{episodes} | Step: {step_count:3d} | Reward: {rewards:5d} | loss: {loss_avg:.4f} | epsilon: {curr_epsilon:.4f} | mission_state: success')
else:
status = (f'Episode: {episode + 1:5d}/{episodes} | Step: {step_count:3d} | Reward: {rewards:5d} | loss: {loss_avg:.4f} | epsilon: {curr_epsilon:.4f} | mission_state: run')
sys.stdout.write('\r' + status)
sys.stdout.flush()
print(f'\r')
eposide_reward.append(rewards)
eposide_loss_avg.append(loss_avg)
if not args.infinite_loop:
episode += 1
agent.save(f"{folder_path}\\model.pth") # save weight
plot_rewards_and_losses(range(1, episode + 1), eposide_reward, eposide_loss_avg, save_path=f'{folder_path}\\final_performance_plot.png')
print("Updated model saved!")
else:
print("The corresponding object cannot be found in the environment and training cannot be started.")