def generate_json(cfg):
flag = True
path = os.path.expanduser('~\Documents\Airsim')
if not os.path.exists(path):
os.makedirs(path)
filename = path + '\settings.json'
data = {}
if cfg.mode == 'move_around':
data['SimMode'] = 'ComputerVision'
else:
data['SettingsVersion'] = 1.2
data['LocalHostIp'] = cfg.ip_address
data['SimMode'] = cfg.SimMode
data['ClockSpeed'] = cfg.ClockSpeed
data["ViewMode"] = "NoDisplay"
PawnPaths = {}
PawnPaths["DefaultQuadrotor"] = {}
PawnPaths["DefaultQuadrotor"][
'PawnBP'] = ''' Class'/AirSim/Blueprints/BP_''' + cfg.drone + '''.BP_''' + cfg.drone + '''_C' '''
data['PawnPaths'] = PawnPaths
# Define agents:
_, reset_array_raw, _, _ = initial_positions(cfg.env_name,
num_agents=cfg.num_agents)
Vehicles = {}
if len(reset_array_raw) < cfg.num_agents:
print(
"Error: Either reduce the number of agents or add more initial positions"
)
flag = False
else:
for agents in range(cfg.num_agents):
name_agent = "drone" + str(agents)
agent_position = reset_array_raw[name_agent].pop(0)
Vehicles[name_agent] = {}
Vehicles[name_agent]["VehicleType"] = "SimpleFlight"
Vehicles[name_agent]["X"] = agent_position[0]
Vehicles[name_agent]["Y"] = agent_position[1]
# Vehicles[name_agent]["Z"] = agent_position[2]
Vehicles[name_agent]["Z"] = 0
Vehicles[name_agent]["Yaw"] = agent_position[3]
data["Vehicles"] = Vehicles
CameraDefaults = {}
CameraDefaults['CaptureSettings'] = []
# CaptureSettings=[]
camera = {}
camera['ImageType'] = 0
camera['Width'] = cfg.width
camera['Height'] = cfg.height
camera['FOV_Degrees'] = cfg.fov_degrees
CameraDefaults['CaptureSettings'].append(camera)
camera = {}
camera['ImageType'] = 3
camera['Width'] = cfg.width
camera['Height'] = cfg.height
camera['FOV_Degrees'] = cfg.fov_degrees
CameraDefaults['CaptureSettings'].append(camera)
data['CameraDefaults'] = CameraDefaults
with open(filename, 'w') as outfile:
json.dump(data, outfile, indent=4)
return flag
def DeepQLearning(cfg):
f_x_val = 5.442914962768555
f_y_val = 1.8743505477905273
if cfg.mode == 'move_around':
env_process, env_folder = start_environment(env_name=cfg.env_name)
else:
algorithm_cfg = read_cfg(config_filename='configs/DeepQLearning.cfg',
verbose=True)
if algorithm_cfg.distributed_algo == 'GlobalLearningGlobalUpdate' or cfg.mode == 'infer':
cfg.num_agents = 1
# Start the environment
env_process, env_folder = start_environment(env_name=cfg.env_name)
# Connect to Unreal Engine and get the drone handle: client
client, old_posit, initZ = connect_drone(ip_address=cfg.ip_address,
phase=cfg.mode,
num_agents=cfg.num_agents)
initial_pos = old_posit.copy()
# Load the initial positions for the environment
reset_array, reset_array_raw, level_name, crash_threshold = initial_positions(
cfg.env_name, initZ, cfg.num_agents)
# Get memory handler
process = psutil.Process(getpid())
# Load PyGame Screen
screen = pygame_connect(phase=cfg.mode)
fig_z = []
fig_nav = []
debug = False
# Generate path where the weights will be saved
cfg, algorithm_cfg = save_network_path(cfg=cfg,
algorithm_cfg=algorithm_cfg)
current_state = {}
new_state = {}
posit = {}
name_agent_list = []
agent = {}
# Replay Memory for RL
if cfg.mode == 'train':
ReplayMemory = {}
target_agent = {}
for drone in range(cfg.num_agents):
name_agent = "drone" + str(drone)
name_agent_list.append(name_agent)
print_orderly(name_agent, 40)
ReplayMemory[name_agent] = Memory(algorithm_cfg.buffer_len)
agent[name_agent] = PedraAgent(algorithm_cfg,
client,
name='DQN',
vehicle_name=name_agent)
target_agent[name_agent] = PedraAgent(algorithm_cfg,
client,
name='Target',
vehicle_name=name_agent)
current_state[name_agent] = agent[name_agent].get_state()
elif cfg.mode == 'infer':
name_agent = 'drone0'
name_agent_list.append(name_agent)
agent[name_agent] = PedraAgent(algorithm_cfg,
client,
name=name_agent + 'DQN',
vehicle_name=name_agent)
env_cfg = read_cfg(config_filename=env_folder + 'config.cfg')
nav_x = []
nav_y = []
altitude = {}
altitude[name_agent] = []
p_z, f_z, fig_z, ax_z, line_z, fig_nav, ax_nav, nav = initialize_infer(
env_cfg=env_cfg, client=client, env_folder=env_folder)
nav_text = ax_nav.text(0, 0, '')
# Initialize variables
iter = 1
# num_collisions = 0
episode = {}
active = True
print_interval = 1
automate = True
choose = False
print_qval = False
last_crash = {}
ret = {}
num_collisions = {}
num_arrive = {}
level = {}
level_state = {}
level_posit = {}
times_switch = {}
last_crash_array = {}
ret_array = {}
distance_array = {}
epi_env_array = {}
log_files = {}
# If the phase is inference force the num_agents to 1
hyphens = '-' * int((80 - len('Log files')) / 2)
print(hyphens + ' ' + 'Log files' + ' ' + hyphens)
for name_agent in name_agent_list:
ret[name_agent] = 0
num_collisions[name_agent] = 0
last_crash[name_agent] = 0
level[name_agent] = 0
episode[name_agent] = 0
level_state[name_agent] = [None] * len(reset_array[name_agent])
level_posit[name_agent] = [None] * len(reset_array[name_agent])
times_switch[name_agent] = 0
last_crash_array[name_agent] = np.zeros(shape=len(
reset_array[name_agent]),
dtype=np.int32)
ret_array[name_agent] = np.zeros(
shape=len(reset_array[name_agent]))
distance_array[name_agent] = np.zeros(
shape=len(reset_array[name_agent]))
epi_env_array[name_agent] = np.zeros(shape=len(
reset_array[name_agent]),
dtype=np.int32)
# Log file
log_path = algorithm_cfg.network_path + '/' + name_agent + '/' + cfg.mode + 'log.txt'
print("Log path: ", log_path)
log_files[name_agent] = open(log_path, 'w')
distance = 0
switch_env = False
print_orderly('Simulation begins', 80)
# save_posit = old_posit
# last_crash_array = np.zeros(shape=len(level_name), dtype=np.int32)
# ret_array = np.zeros(shape=len(level_name))
# distance_array = np.zeros(shape=len(level_name))
# epi_env_array = np.zeros(shape=len(level_name), dtype=np.int32)
while active:
try:
active, automate, algorithm_cfg, client = check_user_input(
active, automate, agent[name_agent], client,
old_posit[name_agent], initZ, fig_z, fig_nav, env_folder,
cfg, algorithm_cfg)
if automate:
if cfg.mode == 'train':
if iter % algorithm_cfg.switch_env_steps == 0:
switch_env = True
else:
switch_env = False
for name_agent in name_agent_list:
start_time = time.time()
if switch_env:
posit1_old = client.simGetVehiclePose(
vehicle_name=name_agent)
times_switch[
name_agent] = times_switch[name_agent] + 1
level_state[name_agent][level[
name_agent]] = current_state[name_agent]
level_posit[name_agent][
level[name_agent]] = posit1_old
last_crash_array[name_agent][
level[name_agent]] = last_crash[name_agent]
ret_array[name_agent][
level[name_agent]] = ret[name_agent]
distance_array[name_agent][
level[name_agent]] = distance
epi_env_array[name_agent][
level[name_agent]] = episode[name_agent]
level[name_agent] = (
level[name_agent] + 1) % len(
reset_array[name_agent])
print(
name_agent + ' :Transferring to level: ',
level[name_agent], ' - ',
level_name[name_agent][level[name_agent]])
if times_switch[name_agent] < len(
reset_array[name_agent]):
reset_to_initial(level[name_agent],
reset_array,
client,
vehicle_name=name_agent)
else:
current_state[name_agent] = level_state[
name_agent][level[name_agent]]
posit1_old = level_posit[name_agent][
level[name_agent]]
reset_to_initial(level[name_agent],
reset_array,
client,
vehicle_name=name_agent)
client.simSetVehiclePose(
posit1_old,
ignore_collison=True,
vehicle_name=name_agent)
time.sleep(0.1)
last_crash[name_agent] = last_crash_array[
name_agent][level[name_agent]]
ret[name_agent] = ret_array[name_agent][
level[name_agent]]
distance = distance_array[name_agent][
level[name_agent]]
episode[name_agent] = epi_env_array[
name_agent][int(level[name_agent] / 3)]
# environ = environ^True
else:
action, action_type, algorithm_cfg.epsilon, qvals = policy(
algorithm_cfg.epsilon,
current_state[name_agent], iter,
algorithm_cfg.epsilon_saturation,
algorithm_cfg.epsilon_model,
algorithm_cfg.wait_before_train,
algorithm_cfg.num_actions,
agent[name_agent])
action_word = translate_action(
action, algorithm_cfg.num_actions)
# Take the action
agent[name_agent].take_action(
action,
algorithm_cfg.num_actions,
SimMode=cfg.SimMode)
# time.sleep(0.05)
new_state[name_agent] = agent[
name_agent].get_state()
new_depth1, thresh = agent[
name_agent].get_depth(cfg)
# Get GPS information
posit[name_agent] = client.simGetVehiclePose(
vehicle_name=name_agent)
position = posit[name_agent].position
old_p = np.array([
old_posit[name_agent].position.x_val,
old_posit[name_agent].position.y_val
])
new_p = np.array(
[position.x_val, position.y_val])
# calculate distance
distance = distance + np.linalg.norm(new_p -
old_p)
old_posit[name_agent] = posit[name_agent]
#reward, crash = agent[name_agent].reward_gen(new_depth1, action, crash_threshold, thresh, debug, cfg)
#reward2, arrive = agent[name_agent].reward_gen2(start_time)
reward, crash, arrive = agent[
name_agent].reward_gen(
new_depth1, action, crash_threshold,
thresh, debug,
posit[name_agent].position.x_val,
posit[name_agent].position.y_val,
f_x_val, f_y_val, start_time, cfg)
ret[name_agent] = ret[name_agent] + reward
#ret[name_agent] = ret[name_agent] + reward2
agent_state = agent[name_agent].GetAgentState()
if agent_state.has_collided:
num_collisions[
name_agent] = num_collisions[
name_agent] + 1
print('crash')
crash = True
reward = -1000
#if (posit[name_agent].position.x_val == f_x_val) and (posit[name_agent].position.y_val == f_y_val):
#num_arrive[name_agent] = num_arrive[name_agent] + 1
#print("Arrived")
#arrive = True
#reward = 1000000
data_tuple = []
data_tuple.append([
current_state[name_agent], action,
new_state[name_agent], reward, crash
])
err = get_errors(data_tuple, choose,
ReplayMemory[name_agent],
algorithm_cfg.input_size,
agent[name_agent],
target_agent[name_agent],
algorithm_cfg.gamma,
algorithm_cfg.Q_clip)
ReplayMemory[name_agent].add(err, data_tuple)
# Train if sufficient frames have been stored
if iter > algorithm_cfg.wait_before_train:
if iter % algorithm_cfg.train_interval == 0:
# Train the RL network
old_states, Qvals, actions, err, idx = minibatch_double(
data_tuple,
algorithm_cfg.batch_size, choose,
ReplayMemory[name_agent],
algorithm_cfg.input_size,
agent[name_agent],
target_agent[name_agent],
algorithm_cfg.gamma,
algorithm_cfg.Q_clip)
for i in range(
algorithm_cfg.batch_size):
ReplayMemory[name_agent].update(
idx[i], err[i])
if print_qval:
print(Qvals)
if choose:
# Double-DQN
target_agent[name_agent].train_n(
old_states, Qvals, actions,
algorithm_cfg.batch_size,
algorithm_cfg.dropout_rate,
algorithm_cfg.learning_rate,
algorithm_cfg.epsilon, iter)
else:
agent[name_agent].train_n(
old_states, Qvals, actions,
algorithm_cfg.batch_size,
algorithm_cfg.dropout_rate,
algorithm_cfg.learning_rate,
algorithm_cfg.epsilon, iter)
# iter += 1
time_exec = time.time() - start_time
mem_percent = process.memory_info()[0] / 2.**30
s_log = '{:<6s} - Level {:>2d} - Iter: {:>6d}/{:<5d} {:<8s}-{:>5s} Eps: {:<1.4f} lr: {:>1.8f} Ret = {:<+6.4f} Last Crash = {:<5d} t={:<1.3f} Mem = {:<5.4f} Reward: {:<+1.4f} '.format(
name_agent, int(level[name_agent]), iter,
episode[name_agent], action_word,
action_type, algorithm_cfg.epsilon,
algorithm_cfg.learning_rate,
ret[name_agent], last_crash[name_agent],
time_exec, mem_percent, reward)
if iter % print_interval == 0:
print(s_log)
log_files[name_agent].write(s_log + '\n')
last_crash[
name_agent] = last_crash[name_agent] + 1
if debug:
cv2.imshow(
name_agent,
np.hstack(
(np.squeeze(
current_state[name_agent],
axis=0),
np.squeeze(new_state[name_agent],
axis=0))))
cv2.waitKey(1)
if crash:
agent[name_agent].return_plot(
ret[name_agent], episode[name_agent],
int(level[name_agent] / 3),
mem_percent, iter, distance)
ret[name_agent] = 0
distance = 0
episode[
name_agent] = episode[name_agent] + 1
last_crash[name_agent] = 0
reset_to_initial(level[name_agent],
reset_array,
client,
vehicle_name=name_agent)
# time.sleep(0.2)
current_state[name_agent] = agent[
name_agent].get_state()
else:
#current_state[name_agent] = new_state[name_agent]
if arrive:
episode[name_agent] = episode[
name_agent] + 1
ret[name_agent] = 0
distance = 0
last_crash[name_agent] = 0
reset_to_initial(
level[name_agent],
reset_array,
client,
vehicle_name=name_agent)
# time.sleep(0.2)
current_state[name_agent] = agent[
name_agent].get_state()
else:
current_state[name_agent] = new_state[
name_agent]
if iter % algorithm_cfg.max_iters == 0:
automate = False
# if iter >140:
# active=False
if iter % algorithm_cfg.communication_interval == 0 and iter > algorithm_cfg.wait_before_train:
print(
'Communicating the weights and averaging them')
communicate_across_agents(agent, name_agent_list,
algorithm_cfg)
communicate_across_agents(target_agent,
name_agent_list,
algorithm_cfg)
if iter % algorithm_cfg.update_target_interval == 0 and iter > algorithm_cfg.wait_before_train:
for name_agent in name_agent_list:
agent[name_agent].take_action(
[-1],
algorithm_cfg.num_actions,
SimMode=cfg.SimMode)
print(name_agent +
' - Switching Target Network')
agent[name_agent].save_network(
algorithm_cfg.network_path)
choose = not choose
iter += 1
elif cfg.mode == 'infer':
# Inference phase
agent_state = agent[name_agent].GetAgentState()
if agent_state.has_collided:
print('Drone collided')
print("Total distance traveled: ",
np.round(distance, 2))
active = False
client.moveByVelocityAsync(
vx=0,
vy=0,
vz=0,
duration=1,
vehicle_name=name_agent).join()
if nav_x: # Nav_x is empty if the drone collides in first iteration
ax_nav.plot(nav_x.pop(),
nav_y.pop(),
'r*',
linewidth=20)
file_path = env_folder + 'results/'
fig_z.savefig(file_path + 'altitude_variation.png',
dpi=500)
fig_nav.savefig(file_path + 'navigation.png',
dpi=500)
close_env(env_process)
print('Figures saved')
else:
posit[name_agent] = client.simGetVehiclePose(
vehicle_name=name_agent)
distance = distance + np.linalg.norm(
np.array([
old_posit[name_agent].position.x_val -
posit[name_agent].position.x_val,
old_posit[name_agent].position.y_val -
posit[name_agent].position.y_val
]))
# altitude[name_agent].append(-posit[name_agent].position.z_val+p_z)
altitude[name_agent].append(
-posit[name_agent].position.z_val - f_z)
quat = (posit[name_agent].orientation.w_val,
posit[name_agent].orientation.x_val,
posit[name_agent].orientation.y_val,
posit[name_agent].orientation.z_val)
yaw = euler_from_quaternion(quat)[2]
x_val = posit[name_agent].position.x_val
y_val = posit[name_agent].position.y_val
z_val = posit[name_agent].position.z_val
nav_x.append(env_cfg.alpha * x_val + env_cfg.o_x)
nav_y.append(env_cfg.alpha * y_val + env_cfg.o_y)
nav.set_data(nav_x, nav_y)
nav_text.remove()
nav_text = ax_nav.text(25,
55,
'Distance: ' +
str(np.round(distance, 2)),
style='italic',
bbox={
'facecolor': 'white',
'alpha': 0.5
})
line_z.set_data(
np.arange(len(altitude[name_agent])),
altitude[name_agent])
ax_z.set_xlim(0, len(altitude[name_agent]))
fig_z.canvas.draw()
fig_z.canvas.flush_events()
current_state[name_agent] = agent[
name_agent].get_state()
action, action_type, algorithm_cfg.epsilon, qvals = policy(
1, current_state[name_agent], iter,
algorithm_cfg.epsilon_saturation, 'inference',
algorithm_cfg.wait_before_train,
algorithm_cfg.num_actions, agent[name_agent])
action_word = translate_action(
action, algorithm_cfg.num_actions)
# Take continuous action
agent[name_agent].take_action(
action,
algorithm_cfg.num_actions,
SimMode=cfg.SimMode)
old_posit[name_agent] = posit[name_agent]
# Verbose and log making
s_log = 'Position = ({:<3.2f},{:<3.2f}, {:<3.2f}) Orientation={:<1.3f} Predicted Action: {:<8s} '.format(
x_val, y_val, z_val, yaw, action_word)
print(s_log)
log_files[name_agent].write(s_log + '\n')
except Exception as e:
if str(
e
) == 'cannot reshape array of size 1 into shape (0,0,3)':
print('Recovering from AirSim error')
client, old_posit, initZ = connect_drone(
ip_address=cfg.ip_address,
phase=cfg.mode,
num_agents=cfg.num_agents)
agent[name_agent].client = client
else:
print('------------- Error -------------')
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(
exc_tb.tb_frame.f_code.co_filename)[1]
print(exc_type, fname, exc_tb.tb_lineno)
print(exc_obj)
automate = False
print('Hit r and then backspace to start from this point')
def DeepQLearning(cfg, env_process, env_folder):
algorithm_cfg = read_cfg(config_filename='configs/DeepQLearning.cfg',
verbose=True)
algorithm_cfg.algorithm = cfg.algorithm
if 'GlobalLearningGlobalUpdate-SA' in algorithm_cfg.distributed_algo:
# algorithm_cfg = update_algorithm_cfg(algorithm_cfg, cfg)
cfg.num_agents = 1
# # Start the environment
# env_process, env_folder = start_environment(env_name=cfg.env_name)
# Connect to Unreal Engine and get the drone handle: client
client, old_posit, initZ = connect_drone(ip_address=cfg.ip_address,
phase=cfg.mode,
num_agents=cfg.num_agents)
print(old_posit)
initial_pos = old_posit.copy()
# Load the initial positions for the environment
# if Target:
if cfg.Target:
print("Traget Iniitial")
reset_array, reset_array_raw, level_name, crash_threshold = initial_positions(
cfg.env_name, initZ, cfg.num_agents)
else:
print("Source Iniitial")
reset_array, reset_array_raw, level_name, crash_threshold = initial_positions2(
cfg.env_name + "2", initZ, cfg.num_agents)
# ""
print("reset_array", reset_array)
print("reset_array_raw", reset_array_raw)
print("level_name", level_name)
print("crash_threshold", crash_threshold)
# Initialize System Handlers
process = psutil.Process(getpid())
# nvidia_smi.nvmlInit()
# Load PyGame Screen
screen = pygame_connect(phase=cfg.mode)
fig_z = []
fig_nav = []
debug = False
# Generate path where the weights will be saved
cfg, algorithm_cfg = save_network_path(cfg=cfg,
algorithm_cfg=algorithm_cfg)
current_state = {}
new_state = {}
posit = {}
name_agent_list = []
agent = {}
# Replay Memory for RL
if cfg.mode == 'train':
ReplayMemory = {}
target_agent = {}
if algorithm_cfg.distributed_algo == 'GlobalLearningGlobalUpdate-MA':
print_orderly('global', 40)
# Multiple agent acts as data collecter and one global learner
global_agent = PedraAgent(algorithm_cfg,
client,
name='DQN',
vehicle_name='global',
Target=cfg.Target,
data_collect=cfg.data_collect)
ReplayMemory = Memory(algorithm_cfg.buffer_len)
target_agent = PedraAgent(algorithm_cfg,
client,
name='Target',
vehicle_name='global',
Target=cfg.Target)
for drone in range(cfg.num_agents):
name_agent = "drone" + str(drone)
name_agent_list.append(name_agent)
print_orderly(name_agent, 40)
# TODO: turn the neural network off if global agent is present
agent[name_agent] = PedraAgent(algorithm_cfg,
client,
name='DQN',
vehicle_name=name_agent,
Target=cfg.Target,
data_collect=cfg.data_collect)
if algorithm_cfg.distributed_algo != 'GlobalLearningGlobalUpdate-MA':
ReplayMemory[name_agent] = Memory(algorithm_cfg.buffer_len)
target_agent[name_agent] = PedraAgent(algorithm_cfg,
client,
name='Target',
vehicle_name=name_agent,
Target=cfg.Target)
current_state[name_agent] = agent[name_agent].get_state()
elif cfg.mode == 'infer':
name_agent = 'drone0'
name_agent_list.append(name_agent)
agent[name_agent] = PedraAgent(algorithm_cfg,
client,
name=name_agent + 'DQN',
vehicle_name=name_agent,
Target=cfg.Target)
env_cfg = read_cfg(config_filename=env_folder + 'config_' +
("Target" if cfg.Target else "Source") + '.cfg')
nav_x = []
nav_y = []
altitude = {}
altitude[name_agent] = []
p_z, f_z, fig_z, ax_z, line_z, fig_nav, ax_nav, nav = initialize_infer(
env_cfg=env_cfg, client=client, env_folder=env_folder)
nav_text = ax_nav.text(0, 0, '')
# Select initial position
reset_to_initial(0, reset_array, client, vehicle_name=name_agent)
old_posit[name_agent] = client.simGetVehiclePose(
vehicle_name=name_agent)
# Initialize variables
iter = 1
# num_collisions = 0
episode = {}
active = True
print_interval = 1
automate = True
choose = False
print_qval = False
last_crash = {}
ret = {}
distance = {}
num_collisions = {}
level = {}
level_state = {}
level_posit = {}
times_switch = {}
last_crash_array = {}
ret_array = {}
distance_array = {}
epi_env_array = {}
log_files = {}
# If the phase is inference force the num_agents to 1
hyphens = '-' * int((80 - len('Log files')) / 2)
print(hyphens + ' ' + 'Log files' + ' ' + hyphens)
for name_agent in name_agent_list:
ret[name_agent] = 0
num_collisions[name_agent] = 0
last_crash[name_agent] = 0
level[name_agent] = 0
episode[name_agent] = 0
level_state[name_agent] = [None] * len(reset_array[name_agent])
level_posit[name_agent] = [None] * len(reset_array[name_agent])
times_switch[name_agent] = 0
last_crash_array[name_agent] = np.zeros(shape=len(
reset_array[name_agent]),
dtype=np.int32)
ret_array[name_agent] = np.zeros(shape=len(reset_array[name_agent]))
distance_array[name_agent] = np.zeros(
shape=len(reset_array[name_agent]))
epi_env_array[name_agent] = np.zeros(shape=len(
reset_array[name_agent]),
dtype=np.int32)
distance[name_agent] = 0
# Log file
log_path = algorithm_cfg.network_path + '/' + name_agent + '/' + cfg.mode + 'log.txt'
print("Log path: ", log_path)
log_files[name_agent] = open(log_path, 'w')
print_orderly('Simulation begins', 80)
while active:
try:
active, automate, algorithm_cfg, client = check_user_input(
active, automate, agent[name_agent], client,
old_posit[name_agent], initZ, fig_z, fig_nav, env_folder, cfg,
algorithm_cfg)
if automate:
if cfg.mode == 'train':
if iter % algorithm_cfg.switch_env_steps == 0:
switch_env = True
else:
switch_env = False
for name_agent in name_agent_list:
start_time = time.time()
if switch_env:
posit1_old = client.simGetVehiclePose(
vehicle_name=name_agent)
times_switch[
name_agent] = times_switch[name_agent] + 1
level_state[name_agent][
level[name_agent]] = current_state[name_agent]
level_posit[name_agent][
level[name_agent]] = posit1_old
last_crash_array[name_agent][
level[name_agent]] = last_crash[name_agent]
ret_array[name_agent][
level[name_agent]] = ret[name_agent]
distance_array[name_agent][
level[name_agent]] = distance[name_agent]
epi_env_array[name_agent][
level[name_agent]] = episode[name_agent]
level[name_agent] = (level[name_agent] + 1) % len(
reset_array[name_agent])
print(name_agent + ' :Transferring to level: ',
level[name_agent], ' - ',
level_name[name_agent][level[name_agent]])
if times_switch[name_agent] < len(
reset_array[name_agent]):
reset_to_initial(level[name_agent],
reset_array,
client,
vehicle_name=name_agent)
else:
current_state[name_agent] = level_state[
name_agent][level[name_agent]]
posit1_old = level_posit[name_agent][
level[name_agent]]
reset_to_initial(level[name_agent],
reset_array,
client,
vehicle_name=name_agent)
client.simSetVehiclePose(
posit1_old,
ignore_collison=True,
vehicle_name=name_agent)
time.sleep(0.1)
last_crash[name_agent] = last_crash_array[
name_agent][level[name_agent]]
ret[name_agent] = ret_array[name_agent][
level[name_agent]]
distance[name_agent] = distance_array[name_agent][
level[name_agent]]
episode[name_agent] = epi_env_array[name_agent][
int(level[name_agent] / 3)]
# environ = environ^True
else:
# TODO: policy from one global agent: DONE
if algorithm_cfg.distributed_algo == 'GlobalLearningGlobalUpdate-MA':
agent_this_drone = global_agent
ReplayMemory_this_drone = ReplayMemory
target_agent_this_drone = target_agent
else:
agent_this_drone = agent[name_agent]
ReplayMemory_this_drone = ReplayMemory[
name_agent]
target_agent_this_drone = target_agent[
name_agent]
action, action_type, algorithm_cfg.epsilon, qvals = policy(
algorithm_cfg.epsilon,
current_state[name_agent], iter,
algorithm_cfg.epsilon_saturation,
algorithm_cfg.epsilon_model,
algorithm_cfg.wait_before_train,
algorithm_cfg.num_actions, agent_this_drone)
action_word = translate_action(
action, algorithm_cfg.num_actions)
# Take the action
agent[name_agent].take_action(
action,
algorithm_cfg.num_actions,
Mode='static')
# time.sleep(0.05)
new_state[name_agent] = agent[
name_agent].get_state()
new_depth1, thresh = agent[
name_agent].get_CustomDepth(cfg)
# Get GPS information
posit[name_agent] = client.simGetVehiclePose(
vehicle_name=name_agent)
position = posit[name_agent].position
old_p = np.array([
old_posit[name_agent].position.x_val,
old_posit[name_agent].position.y_val
])
new_p = np.array([position.x_val, position.y_val])
# calculate distance
distance[name_agent] = distance[
name_agent] + np.linalg.norm(new_p - old_p)
old_posit[name_agent] = posit[name_agent]
reward, crash = agent[name_agent].reward_gen(
new_depth1, action, crash_threshold, thresh,
debug, cfg)
ret[name_agent] = ret[name_agent] + reward
agent_state = agent[name_agent].GetAgentState()
if agent_state.has_collided or distance[
name_agent] < 0.1:
num_collisions[name_agent] = num_collisions[
name_agent] + 1
print('crash')
crash = True
reward = -1
data_tuple = []
data_tuple.append([
current_state[name_agent], action,
new_state[name_agent], reward, crash
])
# TODO: one replay memory global, target_agent, agent: DONE
err = get_errors(
data_tuple, choose, ReplayMemory_this_drone,
algorithm_cfg.input_size, agent_this_drone,
target_agent_this_drone, algorithm_cfg.gamma,
algorithm_cfg.Q_clip)
ReplayMemory_this_drone.add(err, data_tuple)
# Train if sufficient frames have been stored
if iter > algorithm_cfg.wait_before_train:
if iter % algorithm_cfg.train_interval == 0:
# Train the RL network
old_states, Qvals, actions, err, idx = minibatch_double(
data_tuple, algorithm_cfg.batch_size,
choose, ReplayMemory_this_drone,
algorithm_cfg.input_size,
agent_this_drone,
target_agent_this_drone,
algorithm_cfg.gamma,
algorithm_cfg.Q_clip)
# TODO global replay memory: DONE
for i in range(algorithm_cfg.batch_size):
ReplayMemory_this_drone.update(
idx[i], err[i])
if print_qval:
print(Qvals)
# TODO global agent, target_agent: DONE
if choose:
# Double-DQN
target_agent_this_drone.network_model.train_n(
old_states, Qvals, actions,
algorithm_cfg.batch_size,
algorithm_cfg.dropout_rate,
algorithm_cfg.learning_rate,
algorithm_cfg.epsilon, iter)
else:
agent_this_drone.network_model.train_n(
old_states, Qvals, actions,
algorithm_cfg.batch_size,
algorithm_cfg.dropout_rate,
algorithm_cfg.learning_rate,
algorithm_cfg.epsilon, iter)
time_exec = time.time() - start_time
gpu_memory, gpu_utilization, sys_memory = get_SystemStats(
process, cfg.NVIDIA_GPU)
for i in range(0, len(gpu_memory)):
tag_mem = 'GPU' + str(i) + '-Memory-GB'
tag_util = 'GPU' + str(i) + 'Utilization-%'
agent[
name_agent].network_model.log_to_tensorboard(
tag=tag_mem,
group='SystemStats',
value=gpu_memory[i],
index=iter)
agent[
name_agent].network_model.log_to_tensorboard(
tag=tag_util,
group='SystemStats',
value=gpu_utilization[i],
index=iter)
agent[name_agent].network_model.log_to_tensorboard(
tag='Memory-GB',
group='SystemStats',
value=sys_memory,
index=iter)
s_log = '{:<6s} - Level {:>2d} - Iter: {:>6d}/{:<5d} {:<8s}-{:>5s} Eps: {:<1.4f} lr: {:>1.8f} Ret = {:<+6.4f} Last Crash = {:<5d} t={:<1.3f} SF = {:<5.4f} Reward: {:<+1.4f} '.format(
name_agent, int(level[name_agent]), iter,
episode[name_agent], action_word, action_type,
algorithm_cfg.epsilon,
algorithm_cfg.learning_rate, ret[name_agent],
last_crash[name_agent], time_exec,
distance[name_agent], reward)
if iter % print_interval == 0:
print(s_log)
log_files[name_agent].write(s_log + '\n')
last_crash[name_agent] = last_crash[name_agent] + 1
if debug:
cv2.imshow(
name_agent,
np.hstack(
(np.squeeze(current_state[name_agent],
axis=0),
np.squeeze(new_state[name_agent],
axis=0))))
cv2.waitKey(1)
if crash:
if distance[name_agent] < 0.01:
# Drone won't move, reconnect
print(
'Recovering from drone mobility issue')
agent[
name_agent].client, old_posit, initZ = connect_drone(
ip_address=cfg.ip_address,
phase=cfg.mode,
num_agents=cfg.num_agents,
client=client)
else:
agent[
name_agent].network_model.log_to_tensorboard(
tag='Return',
group=name_agent,
value=ret[name_agent],
index=episode[name_agent])
agent[
name_agent].network_model.log_to_tensorboard(
tag='Safe Flight',
group=name_agent,
value=distance[name_agent],
index=episode[name_agent])
ret[name_agent] = 0
distance[name_agent] = 0
episode[
name_agent] = episode[name_agent] + 1
last_crash[name_agent] = 0
reset_to_initial(level[name_agent],
reset_array,
client,
vehicle_name=name_agent)
# time.sleep(0.2)
current_state[name_agent] = agent[
name_agent].get_state()
old_posit[
name_agent] = client.simGetVehiclePose(
vehicle_name=name_agent)
else:
current_state[name_agent] = new_state[
name_agent]
if iter % algorithm_cfg.max_iters == 0:
automate = False
# TODO define and state agents
if iter % algorithm_cfg.update_target_interval == 0 and iter > algorithm_cfg.wait_before_train:
if algorithm_cfg.distributed_algo == 'GlobalLearningGlobalUpdate-MA':
print('global' + ' - Switching Target Network')
global_agent.network_model.save_network(
algorithm_cfg.network_path,
episode[name_agent])
else:
for name_agent in name_agent_list:
agent[name_agent].take_action(
[-1],
algorithm_cfg.num_actions,
Mode='static')
print(name_agent +
' - Switching Target Network')
agent[name_agent].network_model.save_network(
algorithm_cfg.network_path,
episode[name_agent])
choose = not choose
# if iter % algorithm_cfg.communication_interval == 0 and iter > algorithm_cfg.wait_before_train:
# print('Communicating the weights and averaging them')
# communicate_across_agents(agent, name_agent_list, algorithm_cfg)
# communicate_across_agents(target_agent, name_agent_list, algorithm_cfg)
iter += 1
elif cfg.mode == 'infer':
# Inference phase
agent_state = agent[name_agent].GetAgentState()
if agent_state.has_collided:
print('Drone collided')
print("Total distance traveled: ",
np.round(distance[name_agent], 2))
active = False
client.moveByVelocityAsync(
vx=0,
vy=0,
vz=0,
duration=1,
vehicle_name=name_agent).join()
if nav_x: # Nav_x is empty if the drone collides in first iteration
ax_nav.plot(nav_x.pop(),
nav_y.pop(),
'r*',
linewidth=20)
file_path = env_folder + 'results/'
fig_z.savefig(file_path + 'altitude_variation.png',
dpi=500)
fig_nav.savefig(file_path + 'navigation.png', dpi=500)
close_env(env_process)
print('Figures saved')
else:
posit[name_agent] = client.simGetVehiclePose(
vehicle_name=name_agent)
distance[name_agent] = distance[
name_agent] + np.linalg.norm(
np.array([
old_posit[name_agent].position.x_val -
posit[name_agent].position.x_val,
old_posit[name_agent].position.y_val -
posit[name_agent].position.y_val
]))
# altitude[name_agent].append(-posit[name_agent].position.z_val+p_z)
altitude[name_agent].append(
-posit[name_agent].position.z_val - f_z)
quat = (posit[name_agent].orientation.w_val,
posit[name_agent].orientation.x_val,
posit[name_agent].orientation.y_val,
posit[name_agent].orientation.z_val)
yaw = euler_from_quaternion(quat)[2]
x_val = posit[name_agent].position.x_val
y_val = posit[name_agent].position.y_val
z_val = posit[name_agent].position.z_val
nav_x.append(env_cfg.alpha * x_val + env_cfg.o_x)
nav_y.append(env_cfg.alpha * y_val + env_cfg.o_y)
nav.set_data(nav_x, nav_y)
nav_text.remove()
nav_text = ax_nav.text(
25,
55,
'Distance: ' +
str(np.round(distance[name_agent], 2)),
style='italic',
bbox={
'facecolor': 'white',
'alpha': 0.5
})
line_z.set_data(np.arange(len(altitude[name_agent])),
altitude[name_agent])
ax_z.set_xlim(0, len(altitude[name_agent]))
fig_z.canvas.draw()
fig_z.canvas.flush_events()
current_state[name_agent] = agent[
name_agent].get_state()
action, action_type, algorithm_cfg.epsilon, qvals = policy(
1, current_state[name_agent], iter,
algorithm_cfg.epsilon_saturation, 'inference',
algorithm_cfg.wait_before_train,
algorithm_cfg.num_actions, agent[name_agent])
action_word = translate_action(
action, algorithm_cfg.num_actions)
# Take continuous action
agent[name_agent].take_action(
action, algorithm_cfg.num_actions, Mode='static')
old_posit[name_agent] = posit[name_agent]
# Verbose and log making
s_log = 'Position = ({:<3.2f},{:<3.2f}, {:<3.2f}) Orientation={:<1.3f} Predicted Action: {:<8s} '.format(
x_val, y_val, z_val, yaw, action_word)
print(s_log)
log_files[name_agent].write(s_log + '\n')
except Exception as e:
if str(e) == 'cannot reshape array of size 1 into shape (0,0,3)':
print('Recovering from AirSim error')
client, old_posit, initZ = connect_drone(
ip_address=cfg.ip_address,
phase=cfg.mode,
num_agents=cfg.num_agents)
agent[name_agent].client = client
else:
print('------------- Error -------------')
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
print(exc_type, fname, exc_tb.tb_lineno)
print(exc_obj)
automate = False
print('Hit r and then backspace to start from this point')
def DeepREINFORCE(cfg, env_process, env_folder):
algorithm_cfg = read_cfg(config_filename='configs/DeepREINFORCE.cfg',
verbose=True)
algorithm_cfg.algorithm = cfg.algorithm
if 'GlobalLearningGlobalUpdate-SA' in algorithm_cfg.distributed_algo:
# algorithm_cfg = update_algorithm_cfg(algorithm_cfg, cfg)
cfg.num_agents = 1
client = []
# Connect to Unreal Engine and get the drone handle: client
client, old_posit, initZ = connect_drone(ip_address=cfg.ip_address,
phase=cfg.mode,
num_agents=cfg.num_agents,
client=client)
initial_pos = old_posit.copy()
# Load the initial positions for the environment
reset_array, reset_array_raw, level_name, crash_threshold = initial_positions(
cfg.env_name, initZ, cfg.num_agents)
# Initialize System Handlers
process = psutil.Process(getpid())
# Load PyGame Screen
screen = pygame_connect(phase=cfg.mode)
fig_z = []
fig_nav = []
debug = False
# Generate path where the weights will be saved
cfg, algorithm_cfg = save_network_path(cfg=cfg,
algorithm_cfg=algorithm_cfg)
current_state = {}
new_state = {}
posit = {}
name_agent_list = []
data_tuple = {}
agent = {}
epi_num = {}
if cfg.mode == 'train':
iter = {}
wait_for_others = {}
if algorithm_cfg.distributed_algo == 'GlobalLearningGlobalUpdate-MA':
print_orderly('global', 40)
# Multiple agent acts as data collecter and one global learner
global_agent = PedraAgent(algorithm_cfg,
client,
name='DQN',
vehicle_name='global')
for drone in range(cfg.num_agents):
name_agent = "drone" + str(drone)
wait_for_others[name_agent] = False
iter[name_agent] = 1
epi_num[name_agent] = 1
data_tuple[name_agent] = []
name_agent_list.append(name_agent)
print_orderly(name_agent, 40)
# TODO: turn the neural network off if global agent is present
agent[name_agent] = PedraAgent(algorithm_cfg,
client,
name='DQN',
vehicle_name=name_agent)
current_state[name_agent] = agent[name_agent].get_state()
elif cfg.mode == 'infer':
iter = 1
name_agent = 'drone0'
name_agent_list.append(name_agent)
agent[name_agent] = PedraAgent(algorithm_cfg,
client,
name=name_agent + 'DQN',
vehicle_name=name_agent)
env_cfg = read_cfg(config_filename=env_folder + 'config.cfg')
nav_x = []
nav_y = []
altitude = {}
altitude[name_agent] = []
p_z, f_z, fig_z, ax_z, line_z, fig_nav, ax_nav, nav = initialize_infer(
env_cfg=env_cfg, client=client, env_folder=env_folder)
nav_text = ax_nav.text(0, 0, '')
# Select initial position
reset_to_initial(0, reset_array, client, vehicle_name=name_agent)
old_posit[name_agent] = client.simGetVehiclePose(
vehicle_name=name_agent)
# Initialize variables
# iter = 1
# num_collisions = 0
episode = {}
active = True
print_interval = 1
automate = True
choose = False
print_qval = False
last_crash = {}
ret = {}
distance = {}
num_collisions = {}
level = {}
level_state = {}
level_posit = {}
times_switch = {}
last_crash_array = {}
ret_array = {}
distance_array = {}
epi_env_array = {}
log_files = {}
# If the phase is inference force the num_agents to 1
hyphens = '-' * int((80 - len('Log files')) / 2)
print(hyphens + ' ' + 'Log files' + ' ' + hyphens)
for name_agent in name_agent_list:
ret[name_agent] = 0
num_collisions[name_agent] = 0
last_crash[name_agent] = 0
level[name_agent] = 0
episode[name_agent] = 0
level_state[name_agent] = [None] * len(reset_array[name_agent])
level_posit[name_agent] = [None] * len(reset_array[name_agent])
times_switch[name_agent] = 0
last_crash_array[name_agent] = np.zeros(shape=len(
reset_array[name_agent]),
dtype=np.int32)
ret_array[name_agent] = np.zeros(shape=len(reset_array[name_agent]))
distance_array[name_agent] = np.zeros(
shape=len(reset_array[name_agent]))
epi_env_array[name_agent] = np.zeros(shape=len(
reset_array[name_agent]),
dtype=np.int32)
distance[name_agent] = 0
# Log file
log_path = algorithm_cfg.network_path + '/' + name_agent + '/' + cfg.mode + 'log.txt'
print("Log path: ", log_path)
log_files[name_agent] = open(log_path, 'w')
# switch_env = False
print_orderly('Simulation begins', 80)
# save_posit = old_posit
# last_crash_array = np.zeros(shape=len(level_name), dtype=np.int32)
# ret_array = np.zeros(shape=len(level_name))
# distance_array = np.zeros(shape=len(level_name))
# epi_env_array = np.zeros(shape=len(level_name), dtype=np.int32)
while active:
try:
active, automate, algorithm_cfg, client = check_user_input(
active, automate, agent[name_agent], client,
old_posit[name_agent], initZ, fig_z, fig_nav, env_folder, cfg,
algorithm_cfg)
if automate:
if cfg.mode == 'train':
if iter[name_agent] % algorithm_cfg.switch_env_steps == 0:
switch_env = True
else:
switch_env = False
for name_agent in name_agent_list:
while not wait_for_others[name_agent]:
start_time = time.time()
if switch_env:
posit1_old = client.simGetVehiclePose(
vehicle_name=name_agent)
times_switch[
name_agent] = times_switch[name_agent] + 1
level_state[name_agent][level[
name_agent]] = current_state[name_agent]
level_posit[name_agent][
level[name_agent]] = posit1_old
last_crash_array[name_agent][
level[name_agent]] = last_crash[name_agent]
ret_array[name_agent][
level[name_agent]] = ret[name_agent]
distance_array[name_agent][
level[name_agent]] = distance[name_agent]
epi_env_array[name_agent][
level[name_agent]] = episode[name_agent]
level[name_agent] = (
level[name_agent] + 1) % len(
reset_array[name_agent])
print(
name_agent + ' :Transferring to level: ',
level[name_agent], ' - ',
level_name[name_agent][level[name_agent]])
if times_switch[name_agent] < len(
reset_array[name_agent]):
reset_to_initial(level[name_agent],
reset_array,
client,
vehicle_name=name_agent)
else:
current_state[name_agent] = level_state[
name_agent][level[name_agent]]
posit1_old = level_posit[name_agent][
level[name_agent]]
reset_to_initial(level[name_agent],
reset_array,
client,
vehicle_name=name_agent)
client.simSetVehiclePose(
posit1_old,
ignore_collison=True,
vehicle_name=name_agent)
time.sleep(0.1)
last_crash[name_agent] = last_crash_array[
name_agent][level[name_agent]]
ret[name_agent] = ret_array[name_agent][
level[name_agent]]
distance[name_agent] = distance_array[
name_agent][level[name_agent]]
episode[name_agent] = epi_env_array[
name_agent][int(level[name_agent] / 3)]
# environ = environ^True
else:
# TODO: policy from one global agent: DONE
if algorithm_cfg.distributed_algo == 'GlobalLearningGlobalUpdate-MA':
agent_this_drone = global_agent
else:
agent_this_drone = agent[name_agent]
action, action_type = policy_REINFORCE(
current_state[name_agent],
agent_this_drone)
# print('Evaluated Policy')
action_word = translate_action(
action, algorithm_cfg.num_actions)
# Take the action
# agent[name_agent].take_action(action, algorithm_cfg.num_actions, SimMode=cfg.SimMode)
agent[name_agent].take_action(
action,
algorithm_cfg.num_actions,
Mode='static')
# print('Took Action')
new_state[name_agent] = agent[
name_agent].get_state()
# print('got state')
new_depth1, thresh = agent[
name_agent].get_CustomDepth(cfg)
# print('Got state')
# Get GPS information
posit[name_agent] = client.simGetVehiclePose(
vehicle_name=name_agent)
position = posit[name_agent].position
old_p = np.array([
old_posit[name_agent].position.x_val,
old_posit[name_agent].position.y_val
])
new_p = np.array(
[position.x_val, position.y_val])
# calculate distance
distance[name_agent] = distance[
name_agent] + np.linalg.norm(new_p - old_p)
old_posit[name_agent] = posit[name_agent]
# print('calculated distance')
reward, crash = agent[name_agent].reward_gen(
new_depth1, action, crash_threshold,
thresh, debug, cfg)
ret[name_agent] = ret[name_agent] + reward
agent_state = agent[name_agent].GetAgentState()
# print('generated reward')
if agent_state.has_collided or distance[
name_agent] < 0.01:
num_collisions[
name_agent] = num_collisions[
name_agent] + 1
if agent_state.has_collided:
print(
'Crash: Collision detected from environment'
)
else:
print(
'Crash: Collision detected from distance'
)
crash = True
reward = -1
data_tuple[name_agent].append([
current_state[name_agent], action, reward,
crash
])
# Train if episode ends
if crash:
# Don't start another episode unless all agents have ended their episodes
wait_for_others[name_agent] = True
if distance[name_agent] < 0.01:
# Drone won't move, reconnect
print(
'Recovering from drone mobility issue'
)
agent[
name_agent].client, old_posit, initZ = connect_drone(
ip_address=cfg.ip_address,
phase=cfg.mode,
num_agents=cfg.num_agents,
client=client)
agent[
name_agent].client, old_posit, initZ = connect_drone(
ip_address=cfg.ip_address,
phase=cfg.mode,
num_agents=cfg.num_agents,
client=client)
time.sleep(2)
wait_for_others[name_agent] = False
else:
agent[
name_agent].network_model.log_to_tensorboard(
tag='Return',
group=name_agent,
value=ret[name_agent],
index=epi_num[name_agent])
agent[
name_agent].network_model.log_to_tensorboard(
tag='Safe Flight',
group=name_agent,
value=distance[name_agent],
index=epi_num[name_agent])
agent[
name_agent].network_model.log_to_tensorboard(
tag='Episode Length',
group=name_agent,
value=len(
data_tuple[name_agent]),
index=epi_num[name_agent])
# Train episode
train_REINFORCE(
data_tuple[name_agent],
algorithm_cfg.batch_size,
agent_this_drone,
algorithm_cfg.learning_rate,
algorithm_cfg.input_size,
algorithm_cfg.gamma,
epi_num[name_agent])
c = agent_this_drone.network_model.get_vars(
)[15]
agent_this_drone.network_model.log_to_tensorboard(
tag='weight',
group=name_agent,
value=c[0],
index=epi_num[name_agent])
data_tuple[name_agent] = []
epi_num[name_agent] += 1
ret[name_agent] = 0
distance[name_agent] = 0
last_crash[name_agent] = 0
# print('train done, now reset')
reset_to_initial(
level[name_agent],
reset_array,
client,
vehicle_name=name_agent)
# time.sleep(0.2)
current_state[name_agent] = agent[
name_agent].get_state()
old_posit[
name_agent] = client.simGetVehiclePose(
vehicle_name=name_agent)
if epi_num[name_agent] % 100 == 0:
agent_this_drone.network_model.save_network(
algorithm_cfg.network_path,
epi_num[name_agent])
# if all are waiting for others, then we can reset.
if all(wait_for_others.values()):
# Now we can communicate weights
print(
'Communicating the weights and averaging them'
)
communicate_across_agents(
agent, name_agent_list,
algorithm_cfg)
for n in name_agent_list:
wait_for_others[n] = False
else:
current_state[name_agent] = new_state[
name_agent]
time_exec = time.time() - start_time
gpu_memory, gpu_utilization, sys_memory = get_SystemStats(
process, cfg.NVIDIA_GPU)
for i in range(0, len(gpu_memory)):
tag_mem = 'GPU' + str(i) + '-Memory-GB'
tag_util = 'GPU' + str(i) + 'Utilization-%'
agent_this_drone.network_model.log_to_tensorboard(
tag=tag_mem,
group='SystemStats',
value=gpu_memory[i],
index=iter[name_agent])
agent_this_drone.network_model.log_to_tensorboard(
tag=tag_util,
group='SystemStats',
value=gpu_utilization[i],
index=iter[name_agent])
agent_this_drone.network_model.log_to_tensorboard(
tag='Memory-GB',
group='SystemStats',
value=sys_memory,
index=iter[name_agent])
s_log = '{:<6s} - Level {:>2d} - Iter: {:>6d}/{:<5d} {:<8s}-{:>5s} lr: {:>1.8f} Ret = {:>+6.4f} Last Crash = {:<5d} t={:<1.3f} SF = {:<5.4f} Reward: {:<+1.4f} '.format(
name_agent, int(level[name_agent]),
iter[name_agent], epi_num[name_agent],
action_word, action_type,
algorithm_cfg.learning_rate,
ret[name_agent], last_crash[name_agent],
time_exec, distance[name_agent], reward)
if iter[name_agent] % print_interval == 0:
print(s_log)
log_files[name_agent].write(s_log + '\n')
last_crash[
name_agent] = last_crash[name_agent] + 1
if debug:
cv2.imshow(
name_agent,
np.hstack(
(np.squeeze(
current_state[name_agent],
axis=0),
np.squeeze(new_state[name_agent],
axis=0))))
cv2.waitKey(1)
if epi_num[
name_agent] % algorithm_cfg.total_episodes == 0:
print(automate)
automate = False
iter[name_agent] += 1
# if iter % algorithm_cfg.communication_interval == 0:
# print('Communicating the weights and averaging them')
# communicate_across_agents(agent, name_agent_list, algorithm_cfg)
# iter += 1
elif cfg.mode == 'infer':
# Inference phase
agent_state = agent[name_agent].GetAgentState()
if agent_state.has_collided:
print('Drone collided')
print("Total distance traveled: ",
np.round(distance[name_agent], 2))
active = False
client.moveByVelocityAsync(
vx=0,
vy=0,
vz=0,
duration=1,
vehicle_name=name_agent).join()
if nav_x: # Nav_x is empty if the drone collides in first iteration
ax_nav.plot(nav_x.pop(),
nav_y.pop(),
'r*',
linewidth=20)
file_path = env_folder + 'results/'
fig_z.savefig(file_path + 'altitude_variation.png',
dpi=500)
fig_nav.savefig(file_path + 'navigation.png', dpi=500)
close_env(env_process)
print('Figures saved')
else:
posit[name_agent] = client.simGetVehiclePose(
vehicle_name=name_agent)
distance[name_agent] = distance[
name_agent] + np.linalg.norm(
np.array([
old_posit[name_agent].position.x_val -
posit[name_agent].position.x_val,
old_posit[name_agent].position.y_val -
posit[name_agent].position.y_val
]))
# altitude[name_agent].append(-posit[name_agent].position.z_val+p_z)
altitude[name_agent].append(
-posit[name_agent].position.z_val - f_z)
quat = (posit[name_agent].orientation.w_val,
posit[name_agent].orientation.x_val,
posit[name_agent].orientation.y_val,
posit[name_agent].orientation.z_val)
yaw = euler_from_quaternion(quat)[2]
x_val = posit[name_agent].position.x_val
y_val = posit[name_agent].position.y_val
z_val = posit[name_agent].position.z_val
nav_x.append(env_cfg.alpha * x_val + env_cfg.o_x)
nav_y.append(env_cfg.alpha * y_val + env_cfg.o_y)
nav.set_data(nav_x, nav_y)
nav_text.remove()
nav_text = ax_nav.text(
25,
55,
'Distance: ' +
str(np.round(distance[name_agent], 2)),
style='italic',
bbox={
'facecolor': 'white',
'alpha': 0.5
})
line_z.set_data(np.arange(len(altitude[name_agent])),
altitude[name_agent])
ax_z.set_xlim(0, len(altitude[name_agent]))
fig_z.canvas.draw()
fig_z.canvas.flush_events()
current_state[name_agent] = agent[
name_agent].get_state()
action, action_type = policy_REINFORCE(
current_state[name_agent], agent[name_agent])
action_word = translate_action(
action, algorithm_cfg.num_actions)
# Take continuous action
# agent[name_agent].take_action(action, algorithm_cfg.num_actions, Mode='static')
agent[name_agent].take_action(
action, algorithm_cfg.num_actions, Mode='static')
old_posit[name_agent] = posit[name_agent]
# Verbose and log making
s_log = 'Position = ({:<3.2f},{:<3.2f}, {:<3.2f}) Orientation={:<1.3f} Predicted Action: {:<8s} '.format(
x_val, y_val, z_val, yaw, action_word)
print(s_log)
log_files[name_agent].write(s_log + '\n')
except Exception as e:
if str(e) == 'cannot reshape array of size 1 into shape (0,0,3)':
print('Recovering from AirSim error')
client, old_posit, initZ = connect_drone(
ip_address=cfg.ip_address,
phase=cfg.mode,
num_agents=cfg.num_agents,
client=client)
time.sleep(2)
agent[name_agent].client = client
wait_for_others[name_agent] = False
else:
print('------------- Error -------------')
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
print(exc_type, fname, exc_tb.tb_lineno)
print(exc_obj)
automate = False
print('Hit r and then backspace to start from this point')
