def train(arglist):
debug = False
arglist.save_dir = arglist.save_dir + "batch_size" + str(arglist.batch_size) + "buffer_size" + str(arglist.buffer_size)
with U.single_threaded_session():
if debug:
begin = time_begin()
# Create environment
env = make_env(arglist.scenario, arglist, arglist.benchmark)
if debug:
print(time_end(begin, "step 0"))
begin = time_begin()
# Create agent trainers
obs_shape_n = [env.observation_space[i].shape for i in range(env.n)]
num_adversaries = min(env.n, arglist.num_adversaries)
if debug:
print(time_end(begin, "step 1"))
begin = time_begin()
trainers = get_trainers(env, num_adversaries, obs_shape_n, arglist)
print('Using good policy {} and adv policy {}'.format(arglist.good_policy, arglist.adv_policy))
if debug:
print(time_end(begin, "step2"))
begin = time_begin()
efficiency = tf.placeholder(tf.float32, shape=None, name="efficiency_placeholder")
efficiency_summary = tf.summary.scalar("efficiency", efficiency)
p_losses_ph = tf.placeholder(tf.float32, shape=[env.n], name="p_loss")
p_losses_summary = tf.summary.histogram("loss", p_losses_ph)
q_losses_ph = tf.placeholder(tf.float32, shape=[env.n], name="q_loss")
q_losses_summary = tf.summary.histogram("loss", q_losses_ph)
loss_summary = tf.summary.merge([q_losses_summary, p_losses_summary], name="loss")
writer = tf.summary.FileWriter("../summary/efficiency")
writer2 = tf.summary.FileWriter("../summary/loss")
# Initialize
U.initialize()
if debug:
print(time_end(begin, "step3"))
begin = time_begin()
# Load previous results, if necessary
if arglist.load_dir == "":
arglist.load_dir = arglist.save_dir
if arglist.display or arglist.restore or arglist.benchmark:
print('Loading previous state...')
U.load_state(arglist.load_dir)
if not os.path.exists(arglist.save_dir):
os.makedirs(arglist.save_dir)
episode_rewards = [0.0] # sum of rewards for all agents
agent_rewards = [[0.0] for _ in range(env.n)] # individual agent reward
final_ep_rewards = [] # sum of rewards for training curve
final_ep_ag_rewards = [] # agent rewards for training curve
agent_info = [[[]]] # placeholder for benchmarking info
model_number = int(arglist.num_episodes / arglist.save_rate)
saver = tf.train.Saver(max_to_keep=model_number)
episode_step = 0
train_step = 0
t_start = time.time()
# custom statistics variable------------------------------------------------------------------------------------
loss_all = []
aver_cover = []
j_index = []
instantaneous_accmulated_reward = []
instantaneous_dis = []
instantaneous_out_the_map = []
# q_value = []
energy_consumptions_for_test = []
bl_coverage = 0.8
bl_jainindex = 0.8
bl_loss = 100
energy_efficiency = []
over_map_counter = 0
over_map_one_episode = []
aver_cover_one_episode = []
j_index_one_episode = []
disconnected_number_counter = 0
disconnected_number_one_episode = []
accmulated_reward_one_episode = []
actions = []
energy_one_episode = []
route = []
obs_n = env.reset()
episode_reward_step = 0
model_name = arglist.load_dir.split('/')[-3] + '/' + arglist.load_dir.split('/')[-2] + '/'
if FLAGS.greedy_action:
model_name = model_name + 'greedy/'
elif FLAGS.random_action:
model_name = model_name + 'random/'
# if debug:
# print(time_end(begin, "initialize"))
# begin = time_begin()
print('Starting iterations...')
episode_begin_time = time.time()
while True:
# get action
action_n = [agent.action(obs) for agent, obs in zip(trainers, obs_n)]
# environment step
new_obs_n, rew_n, done_n, info_n = env.step(action_n)
episode_step += 1
done = all(done_n)
terminal = (episode_step >= arglist.max_episode_len)
# collect experience
for i, agent in enumerate(trainers):
agent.experience(obs_n[i], action_n[i], rew_n[i], new_obs_n[i], done_n[i], terminal)
obs_n = new_obs_n
for i, rew in enumerate(rew_n):
episode_rewards[-1] += rew
agent_rewards[i][-1] += rew
# increment custom statistics variables in the epoch--------------------------------------------------------
episode_reward_step += np.mean(rew_n)
j_index_one_episode.append(env._get_jain_index())
over_map_counter += env._get_over_map()
over_map_one_episode.append(over_map_counter)
disconnected_number_counter += env._get_dis()
disconnected_number_one_episode.append(disconnected_number_counter)
aver_cover_one_episode.append(env._get_aver_cover())
energy_one_episode.append(env._get_energy())
s_route = env._get_state()
for route_i in range(0, FLAGS.num_uav * 2, 2):
tmp = [s_route[route_i], s_route[route_i + 1]]
route.append(tmp)
accmulated_reward_one_episode.append(episode_reward_step)
# if debug:
# print(time_end(begin, "others"))
# begin = time_begin()
if done or terminal:
model_name = arglist.save_dir.split('/')[-1] + '/'
episode_number = int(train_step / arglist.max_episode_len)
temp_efficiency = np.array(aver_cover_one_episode) * np.array(
j_index_one_episode) / np.array(energy_one_episode)
draw_util.draw_single_episode(arglist.pictures_dir_train + model_name + "single_episode/",
episode_number,
temp_efficiency,
aver_cover_one_episode,
j_index_one_episode,
energy_one_episode,
disconnected_number_one_episode,
over_map_one_episode,
accmulated_reward_one_episode)
# reset custom statistics variabl between episode and epoch---------------------------------------------
instantaneous_accmulated_reward.append(accmulated_reward_one_episode[-1])
j_index.append(j_index_one_episode[-1])
instantaneous_dis.append(disconnected_number_one_episode[-1])
instantaneous_out_the_map.append(over_map_one_episode[-1])
aver_cover.append(aver_cover_one_episode[-1])
energy_consumptions_for_test.append(energy_one_episode[-1])
energy_efficiency.append(aver_cover_one_episode[-1] * j_index_one_episode[-1] / energy_one_episode[-1])
episode_end_time = time.time()
# plot fig
efficiency_s = tf.get_default_session().run(efficiency_summary, feed_dict={efficiency: energy_efficiency[episode_number]})
writer.add_summary(efficiency_s, global_step=episode_number)
# plt fig
print('Episode: %d - energy_consumptions: %s, efficiency: %s, time %s' % (train_step / arglist.max_episode_len,
str(env._get_energy_origin()),
str(energy_efficiency[-1]),
str(round(episode_end_time - episode_begin_time, 3))))
episode_begin_time = episode_end_time
# draw picture of this episode
if arglist.draw_picture_test and aver_cover[-1] >= bl_coverage and j_index[-1] >= bl_jainindex \
and instantaneous_dis[-1] <= bl_loss:
episode_number_name = 'episode_' + str(episode_number)
draw_util.draw(episode_number_name, arglist.pictures_dir_test + model_name, energy_one_episode,
route, actions, aver_cover_one_episode, j_index_one_episode,
accmulated_reward_one_episode, disconnected_number_one_episode, over_map_one_episode,
arglist.max_episode_len)
j_index_one_episode = []
over_map_counter = 0
over_map_one_episode = []
disconnected_number_counter = 0
disconnected_number_one_episode = []
aver_cover_one_episode = []
energy_one_episode = []
route = []
episode_reward_step = 0
accmulated_reward_one_episode = []
if arglist.draw_picture_test:
if len(episode_rewards) % arglist.save_rate == 0:
episode_number_name = train_step / arglist.max_episode_len
draw_util.drawTest(episode_number_name,
arglist.pictures_dir_train + model_name,
energy_consumptions_for_test,
aver_cover, j_index,
instantaneous_accmulated_reward, instantaneous_dis, instantaneous_out_the_map
, len(aver_cover), bl_coverage, bl_jainindex, bl_loss, energy_efficiency, False)
# reset custom statistics variabl between episode and epoch---------------------------------------------
obs_n = env.reset()
episode_step = 0
episode_rewards.append(0)
for a in agent_rewards:
a.append(0)
agent_info.append([[]])
# increment global step counter
train_step += 1
# for benchmarking learned policies
if arglist.benchmark:
for i, info in enumerate(info_n):
agent_info[-1][i].append(info_n['n'])
if train_step > arglist.benchmark_iters and (done or terminal):
file_name = arglist.benchmark_dir + arglist.exp_name + '.pkl'
print('Finished benchmarking, now saving...')
with open(file_name, 'wb') as fp:
pickle.dump(agent_info[:-1], fp)
break
continue
# for displaying learned policies
if arglist.draw_picture_test:
if len(episode_rewards) > arglist.num_episodes:
break
continue
if arglist.display:
time.sleep(0.1)
env.render()
continue
# update all trainers, if not in display or benchmark mode
p_loss_list = []
q_loss_list = []
for agent in trainers:
agent.preupdate()
for agent in trainers:
temp = agent.update(trainers, train_step)
if temp is not None:
p_loss_list.append(temp[1])
q_loss_list.append(temp[0])
if len(p_loss_list) == env.n:
loss_s = tf.get_default_session().run(loss_summary,
feed_dict={p_losses_ph: p_loss_list,
q_losses_ph: q_loss_list})
writer2.add_summary(loss_s, global_step=train_step)
# save model, display training output
if terminal and (len(episode_rewards) % arglist.save_rate == 0):
episode_number_name = train_step / arglist.max_episode_len
save_dir_custom = arglist.save_dir + "/" + str(episode_number_name) + '/'
# save_dir
U.save_state(save_dir_custom, saver=saver)
# print statement depends on whether or not there are adversaries
if num_adversaries == 0:
print("steps: {}, episodes: {}, mean episode reward: {}, time: {}".format(
train_step, len(episode_rewards), np.mean(episode_rewards[-arglist.save_rate:]), round(time.time()-t_start, 3)))
else:
print("steps: {}, episodes: {}, mean episode reward: {}, agent episode reward: {}, time: {}".format(
train_step, len(episode_rewards), np.mean(episode_rewards[-arglist.save_rate:]),
[np.mean(rew[-arglist.save_rate:]) for rew in agent_rewards], round(time.time()-t_start, 3)))
t_start = time.time()
# Keep track of final episode reward
final_ep_rewards.append(np.mean(episode_rewards[-arglist.save_rate:]))
for rew in agent_rewards:
final_ep_ag_rewards.append(np.mean(rew[-arglist.save_rate:]))
# draw custom statistics picture when save the model----------------------------------------------------
if arglist.draw_picture_train:
episode_number_name = train_step / arglist.max_episode_len
model_name = arglist.save_dir.split('/')[-1] + '/'
draw_util.draw_episodes(episode_number_name,
arglist.pictures_dir_train + model_name + "all_episodes/",
aver_cover,
j_index,
energy_consumptions_for_test,
instantaneous_dis,
instantaneous_out_the_map,
energy_efficiency,
instantaneous_accmulated_reward,
len(aver_cover))
# saves final episode reward for plotting training curve later
if len(episode_rewards) > arglist.num_episodes:
rew_file_name = arglist.plots_dir + arglist.exp_name + '_rewards.pkl'
with open(rew_file_name, 'wb') as fp:
pickle.dump(final_ep_rewards, fp)
agrew_file_name = arglist.plots_dir + arglist.exp_name + '_agrewards.pkl'
with open(agrew_file_name, 'wb') as fp:
pickle.dump(final_ep_ag_rewards, fp)
print('...Finished total of {} episodes.'.format(len(episode_rewards)))
break
def test(arglist):
debug = False
num_tasks = arglist.num_task # 总共有多少个任务
list_of_taskenv = [] # env list
load_path = arglist.load_dir
with U.single_threaded_session():
if debug:
begin = time_begin()
# 1.1创建每个任务的actor trainer和critic trainer
trainers_list = []
env = make_env(arglist.scenario, arglist.benchmark)
# Create agent trainers
obs_shape_n = [env.observation_space[i].shape for i in range(env.n)]
num_adversaries = min(env.n, arglist.num_adversaries)
for i in range(num_tasks):
list_of_taskenv.append(make_env(arglist.scenario))
trainers = get_trainers(list_of_taskenv[i],
"task_" + str(i + 1) + "_",
num_adversaries,
obs_shape_n,
arglist)
trainers_list.append(trainers)
print('Using good policy {} and adv policy {}'.format(arglist.good_policy, arglist.adv_policy))
global_steps_tensor = tf.Variable(tf.zeros(num_tasks), trainable=False) # global timesteps for each env
global_steps_ph = tf.placeholder(tf.float32, [num_tasks])
global_steps_assign_op = tf.assign(global_steps_tensor, global_steps_ph)
model_number = int(arglist.num_episodes / arglist.save_rate)
saver = tf.train.Saver(max_to_keep=model_number)
efficiency_list = []
for i in range(num_tasks):
efficiency_list.append(tf.placeholder(tf.float32, shape=None, name="efficiency_placeholder" + str(i)))
efficiency_summary_list = []
for i in range(num_tasks):
efficiency_summary_list.append(tf.summary.scalar("efficiency_%s" % i, efficiency_list[i]))
writer = tf.summary.FileWriter("../summary/efficiency")
# Initialize
U.initialize()
for var in tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES):
print(var)
if debug:
print(time_end(begin, "initialize"))
begin = time_begin()
model_name = arglist.load_dir.split('/')[-2] + '/'
mkdir(arglist.pictures_dir_test + model_name)
model_index_step = 0
model_number_total = arglist.train_num_episodes / arglist.save_rate
max_model_index = 0
max_average_energy_efficiency = 0
while True:
if model_index_step >= model_number_total:
with open(arglist.pictures_dir_test + model_name + 'test_report' + '.txt', 'a+') as file:
report = '\nModel ' + str(max_model_index) + ' attained max average energy efficiency' + \
'\nMax average energy efficiency:' + str(max_average_energy_efficiency)
file.write(report)
break
else:
model_index_step += 1
# 1.4 加载checkpoints
if arglist.load_dir == "":
arglist.load_dir = arglist.save_dir
if arglist.display or arglist.restore or arglist.benchmark:
print('Loading previous state...')
model_load_dir = arglist.load_dir + str(model_index_step * arglist.save_rate - 1) + '/'
U.load_state(arglist.load_dir)
# global_steps = tf.get_default_session().run(global_steps_tensor)
# 1.5 初始化ENV
obs_n_list = []
for i in range(num_tasks):
obs_n = list_of_taskenv[i].reset()
obs_n_list.append(obs_n)
# 1.2 全局变量初始化
episodes_rewards = [[0.0] for _ in range(num_tasks)] # 每个元素为在一个episode中所有agents rewards的和
# agent_rewards[i]中的每个元素记录单个agent在一个episode中所有rewards的和
agent_rewards = [[[0.0] for _ in range(env.n)] for _ in range(num_tasks)]
final_ep_rewards = [[] for _ in range(num_tasks)] # sum of rewards for training curve
final_ep_ag_rewards = [[] for _ in range(num_tasks)] # agent rewards for training curve
energy_consumptions_for_test = [[] for _ in range(num_tasks)]
j_index = [[] for _ in range(num_tasks)]
aver_cover = [[] for _ in range(num_tasks)]
instantaneous_dis = [[] for _ in range(num_tasks)]
instantaneous_out_the_map = [[] for _ in range(num_tasks)]
energy_efficiency = [[] for _ in range(num_tasks)]
instantaneous_accmulated_reward = [[] for _ in range(num_tasks)]
# 1.3 局部变量初始化
local_steps = np.zeros(num_tasks) # local timesteps for each env
energy_one_episode = [[] for _ in range(num_tasks)]
j_index_one_episode = [[] for _ in range(num_tasks)]
aver_cover_one_episode = [[] for _ in range(num_tasks)]
over_map_counter = np.zeros(num_tasks)
over_map_one_episode = [[] for _ in range(num_tasks)]
disconnected_number_counter = np.zeros(num_tasks)
disconnected_number_one_episode = [[] for _ in range(num_tasks)]
episode_reward_step = np.zeros(num_tasks) # 累加一个episode里每一步的所有智能体的平均reward
accmulated_reward_one_episode = [[] for _ in range(num_tasks)]
route_one_episode = [[] for _ in range(num_tasks)]
bl_coverage = 0.8
bl_jainindex = 0.8
bl_loss = 100
energy_efficiency = []
print('Starting iterations...')
while True:
for task_index in range(num_tasks):
# 2.1更新环境，采集样本
current_env = list_of_taskenv[task_index]
current_trainers = trainers_list[task_index]
# get action
action_n = [agent.action(obs) for agent, obs in zip(trainers, obs_n)]
# environment step
new_obs_n, rew_n, done_n, info_n = current_env.step(action_n)
if debug:
print(time_end(begin, "env.step"))
begin = time_begin()
local_steps[task_index] += 1 # 更新局部计数器
global_steps[task_index] += 1 # 更新全局计数器
done = all(done_n)
terminal = (local_steps[task_index] >= arglist.max_episode_len)
# 收集experience
for i in range(env.n):
current_trainers[i].experience(obs_n_list[task_index][i], action_n[i], rew_n[i], new_obs_n[i],
done_n[i], terminal)
# 更新obs
obs_n_list[task_index] = new_obs_n
# 更新reward
for i, rew in enumerate(rew_n):
episodes_rewards[task_index][-1] += rew
agent_rewards[task_index][i][-1] += rew
# energy
energy_one_episode[task_index].append(current_env.get_energy())
# fair index
j_index_one_episode[task_index].append(current_env.get_jain_index())
# coverage
aver_cover_one_episode[task_index].append(current_env.get_aver_cover())
# over map counter
over_map_counter[task_index] += current_env.get_over_map()
over_map_one_episode[task_index].append(over_map_counter[task_index])
# disconnected counter
disconnected_number_counter[task_index] += current_env.get_dis()
disconnected_number_one_episode[task_index].append(disconnected_number_counter[task_index])
# reward
episode_reward_step[task_index] += np.mean(rew_n)
accmulated_reward_one_episode[task_index].append(episode_reward_step[task_index])
route = current_env.get_agent_pos()
route_one_episode[task_index].append(route)
if done or terminal:
# reset custom statistics variabl between episode and epoch---------------------------------------------
instantaneous_accmulated_reward.append(accmulated_reward_one_episode[-1])
j_index.append(j_index_one_episode[-1])
instantaneous_dis.append(disconnected_number_one_episode[-1])
instantaneous_out_the_map.append(over_map_one_episode[-1])
aver_cover.append(aver_cover_one_episode[-1])
energy_consumptions_for_test.append(energy_one_episode[-1])
energy_efficiency.append(aver_cover_one_episode[-1] * j_index_one_episode[-1] / energy_one_episode[-1])
print('Episode: %d - energy_consumptions: %s ' % (train_step / arglist.max_episode_len,
str(env._get_energy_origin())))
if task_index == num_tasks - 1:
energy_one_episode = [[] for _ in range(num_tasks)]
j_index_one_episode = [[] for _ in range(num_tasks)]
aver_cover_one_episode = [[] for _ in range(num_tasks)]
over_map_counter = np.zeros(num_tasks)
over_map_one_episode = [[] for _ in range(num_tasks)]
disconnected_number_counter = np.zeros(num_tasks)
disconnected_number_one_episode = [[] for _ in range(num_tasks)]
episode_reward_step = np.zeros(num_tasks)
accmulated_reward_one_episode = [[] for _ in range(num_tasks)]
route_one_episode = [[] for _ in range(num_tasks)]
if arglist.draw_picture_test:
if len(episode_rewards) % arglist.save_rate == 0:
if np.mean(energy_efficiency) > max_average_energy_efficiency:
max_model_index = model_index_step * arglist.save_rate - 1
max_average_energy_efficiency = np.mean(energy_efficiency)
with open(arglist.pictures_dir_test + model_name + 'test_report' + '.txt', 'a+') as file:
report = '\nModel-' + str(model_index_step * arglist.save_rate - 1) + \
'-testing ' + str(arglist.num_episodes) + ' episodes\'s result:' + \
'\nAverage average attained coverage: ' + str(np.mean(aver_cover)) + \
'\nAverage Jaint\'s fairness index: ' + str(np.mean(j_index)) + \
'\nAverage normalized average energy consumptions:' + str(np.mean(energy_consumptions_for_test)) + \
'\nAverage energy efficiency:' + str(np.mean(energy_efficiency)) + '\n'
file.write(report)
draw_util.drawTest(model_index_step * arglist.save_rate - 1, arglist.pictures_dir_test + model_name,
energy_consumptions_for_test, aver_cover, j_index,
instantaneous_accmulated_reward, instantaneous_dis, instantaneous_out_the_map
, len(aver_cover), bl_coverage, bl_jainindex, bl_loss, energy_efficiency, False)
# reset custom statistics variabl between episode and epoch----------------------------------------
# for displaying learned policies
if arglist.draw_picture_test:
if len(episode_rewards) > arglist.num_episodes:
break
continue
# saves final episode reward for plotting training curve later
if len(episode_rewards) > arglist.num_episodes:
rew_file_name = arglist.plots_dir + arglist.exp_name + '_rewards.pkl'
with open(rew_file_name, 'wb') as fp:
pickle.dump(final_ep_rewards, fp)
agrew_file_name = arglist.plots_dir + arglist.exp_name + '_agrewards.pkl'
with open(agrew_file_name, 'wb') as fp:
pickle.dump(final_ep_ag_rewards, fp)
print('...Finished total of {} episodes.'.format(len(episode_rewards)))
break
def test(arglist):
with U.single_threaded_session():
# Create environment
env = make_env(arglist.scenario, arglist, arglist.benchmark)
# Create agent trainers
obs_shape_n = [env.observation_space[i].shape for i in range(env.n)]
num_adversaries = min(env.n, arglist.num_adversaries)
trainers = get_trainers(env, num_adversaries, obs_shape_n, arglist)
print('Using good policy {} and adv policy {}'.format(
arglist.good_policy, arglist.adv_policy))
model_name = arglist.load_dir.split('/')[-2] + '/'
if FLAGS.greedy_action:
model_name = model_name + 'greedy/'
elif FLAGS.random_action:
model_name = model_name + 'random/'
model_index_step = 0
model_number_total = arglist.train_num_episodes / arglist.save_rate
max_model_index = 0
max_average_energy_efficiency = 0
draw_util.mkdir(arglist.pictures_dir_test + model_name)
while True:
# Initialize
U.initialize()
if model_index_step >= model_number_total:
with open(
arglist.pictures_dir_test + model_name +
'test_report' + '.txt', 'a+') as file:
report = '\nModel ' + str(max_model_index) + ' attained max average energy efficiency' + \
'\nMax average energy efficiency:' + str(max_average_energy_efficiency)
file.write(report)
break
else:
model_index_step += 1
# Load previous results, if necessary
if arglist.load_dir == "":
arglist.load_dir = arglist.save_dir
if arglist.display or arglist.restore or arglist.benchmark:
print('Loading previous state...')
#model_load_dir = arglist.load_dir + str(model_index_step * arglist.save_rate - 1) + '/'
model_load_dir = arglist.load_dir + str(3299) + '/'
U.load_state(model_load_dir)
episode_rewards = [0.0] # sum of rewards for all agents
agent_rewards = [[0.0]
for _ in range(env.n)] # individual agent reward
final_ep_rewards = [] # sum of rewards for training curve
final_ep_ag_rewards = [] # agent rewards for training curve
agent_info = [[[]]] # placeholder for benchmarking info
saver = tf.train.Saver()
obs_n = env.reset()
episode_step = 0
train_step = 0
t_start = time.time()
# custom statistics variable--------------------------------------------------------------------------------
loss_all = []
aver_cover = []
j_index = []
instantaneous_accmulated_reward = []
instantaneous_dis = []
instantaneous_out_the_map = []
# q_value = []
energy_consumptions_for_test = []
bl_coverage = 0.8
bl_jainindex = 0.8
bl_loss = 100
energy_efficiency = []
over_map_counter = 0
over_map_one_episode = []
aver_cover_one_episode = []
j_index_one_episode = []
disconnected_number_counter = 0
disconnected_number_one_episode = []
accmulated_reward_one_episode = []
actions = []
energy_one_episode = []
route = []
episode_reward_step = 0
print('Starting iterations...')
route_dict = {}
for i in range(FLAGS.num_uav):
key_temp = "UAV" + str(i + 1)
route_dict[key_temp] = []
while True:
# get action
action_n = [
agent.action(obs) for agent, obs in zip(trainers, obs_n)
]
# environment step
new_obs_n, rew_n, done_n, info_n = env.step(action_n)
episode_step += 1
done = all(done_n)
terminal = (episode_step >= arglist.max_episode_len)
# collect experience
for i, agent in enumerate(trainers):
agent.experience(obs_n[i], action_n[i], rew_n[i],
new_obs_n[i], done_n[i], terminal)
obs_n = new_obs_n
for i, rew in enumerate(rew_n):
episode_rewards[-1] += rew
agent_rewards[i][-1] += rew
# increment custom statistics variables in the epoch--------------------------------------------------------
episode_reward_step += np.mean(rew_n)
j_index_one_episode.append(env._get_jain_index())
over_map_counter += env._get_over_map()
over_map_one_episode.append(over_map_counter)
disconnected_number_counter += env._get_dis()
disconnected_number_one_episode.append(
disconnected_number_counter)
aver_cover_one_episode.append(env._get_aver_cover())
energy_one_episode.append(env._get_energy())
s_route = env._get_state()
for index, route_i in enumerate(range(0, FLAGS.num_uav * 2,
2)):
# for piao zong
route_dict["UAV" + str(index + 1)].append(
[s_route[route_i], s_route[route_i + 1]])
# for piao zong
accmulated_reward_one_episode.append(episode_reward_step)
if done or terminal:
### for piaozong
uav_poss_file = "~/UAVNumber_" + str(
FLAGS.num_uav) + ".json"
route_str = json.dumps(route_dict)
with open(uav_poss_file, "w+") as f:
f.write(route_str)
### for piaozong
# reset custom statistics variabl between episode and epoch---------------------------------------------
instantaneous_accmulated_reward.append(
accmulated_reward_one_episode[-1])
j_index.append(j_index_one_episode[-1])
instantaneous_dis.append(
disconnected_number_one_episode[-1])
instantaneous_out_the_map.append(over_map_one_episode[-1])
aver_cover.append(aver_cover_one_episode[-1])
energy_consumptions_for_test.append(energy_one_episode[-1])
energy_efficiency.append(aver_cover_one_episode[-1] *
j_index_one_episode[-1] /
energy_one_episode[-1])
print('Episode: %d - energy_consumptions: %s ' %
(train_step / arglist.max_episode_len,
str(env._get_energy_origin())))
j_index_one_episode = []
over_map_counter = 0
over_map_one_episode = []
disconnected_number_counter = 0
disconnected_number_one_episode = []
aver_cover_one_episode = []
energy_one_episode = []
route = []
episode_reward_step = 0
accmulated_reward_one_episode = []
if arglist.draw_picture_test:
if len(episode_rewards) % arglist.save_rate == 0:
if np.mean(energy_efficiency
) > max_average_energy_efficiency:
max_model_index = model_index_step * arglist.save_rate - 1
max_average_energy_efficiency = np.mean(
energy_efficiency)
with open(
arglist.pictures_dir_test + model_name +
'test_report' + '.txt', 'a+') as file:
report = '\nModel-' + str(model_index_step * arglist.save_rate - 1) + \
'-testing ' + str(arglist.num_episodes) + ' episodes\'s result:' + \
'\nAverage average attained coverage: ' + str(np.mean(aver_cover)) + \
'\nAverage Jaint\'s fairness index: ' + str(np.mean(j_index)) + \
'\nAverage normalized average energy consumptions:' + str(np.mean(energy_consumptions_for_test)) + \
'\nAverage energy efficiency:' + str(np.mean(energy_efficiency)) + '\n'
file.write(report)
draw_util.drawTest(
model_index_step * arglist.save_rate - 1,
arglist.pictures_dir_test + model_name,
energy_consumptions_for_test, aver_cover,
j_index, instantaneous_accmulated_reward,
instantaneous_dis, instantaneous_out_the_map,
len(aver_cover), bl_coverage, bl_jainindex,
bl_loss, energy_efficiency, False)
# reset custom statistics variabl between episode and epoch-----------------------------------------
obs_n = env.reset()
episode_step = 0
episode_rewards.append(0)
for a in agent_rewards:
a.append(0)
agent_info.append([[]])
# increment global step counter
train_step += 1
# for benchmarking learned policies
if arglist.benchmark:
for i, info in enumerate(info_n):
agent_info[-1][i].append(info_n['n'])
if train_step > arglist.benchmark_iters and (done
or terminal):
file_name = arglist.benchmark_dir + arglist.exp_name + '.pkl'
print('Finished benchmarking, now saving...')
with open(file_name, 'wb') as fp:
pickle.dump(agent_info[:-1], fp)
break
continue
# for displaying learned policies
if arglist.draw_picture_test:
if len(episode_rewards) > arglist.num_episodes:
break
continue
if arglist.display:
time.sleep(0.1)
env.render()
continue
# update all trainers, if not in display or benchmark mode
loss = None
for agent in trainers:
agent.preupdate()
for agent in trainers:
loss = agent.update(trainers, train_step)
# save model, display training output
if terminal and (len(episode_rewards) % arglist.save_rate
== 0):
episode_number_name = train_step / arglist.max_episode_len
save_dir_custom = arglist.save_dir + str(
episode_number_name) + '/'
U.save_state(save_dir_custom, saver=saver)
# print statement depends on whether or not there are adversaries
if num_adversaries == 0:
print(
"steps: {}, episodes: {}, mean episode reward: {}, time: {}"
.format(
train_step, len(episode_rewards),
np.mean(episode_rewards[-arglist.save_rate:]),
round(time.time() - t_start, 3)))
else:
print(
"steps: {}, episodes: {}, mean episode reward: {}, agent episode reward: {}, time: {}"
.format(
train_step, len(episode_rewards),
np.mean(episode_rewards[-arglist.save_rate:]),
[
np.mean(rew[-arglist.save_rate:])
for rew in agent_rewards
], round(time.time() - t_start, 3)))
t_start = time.time()
# Keep track of final episode reward
final_ep_rewards.append(
np.mean(episode_rewards[-arglist.save_rate:]))
for rew in agent_rewards:
final_ep_ag_rewards.append(
np.mean(rew[-arglist.save_rate:]))
# draw custom statistics picture when save the model----------------------------------------------------
if arglist.draw_picture_train:
episode_number_name = train_step / arglist.max_episode_len
model_name = arglist.save_dir.split('/')[-2] + '/'
draw_util.draw_episode(
episode_number_name,
arglist.pictures_dir_train + model_name,
aver_cover, j_index,
instantaneous_accmulated_reward, instantaneous_dis,
instantaneous_out_the_map, loss_all,
len(aver_cover))
# saves final episode reward for plotting training curve later
if len(episode_rewards) > arglist.num_episodes:
rew_file_name = arglist.plots_dir + arglist.exp_name + '_rewards.pkl'
with open(rew_file_name, 'wb') as fp:
pickle.dump(final_ep_rewards, fp)
agrew_file_name = arglist.plots_dir + arglist.exp_name + '_agrewards.pkl'
with open(agrew_file_name, 'wb') as fp:
pickle.dump(final_ep_ag_rewards, fp)
print('...Finished total of {} episodes.'.format(
len(episode_rewards)))
break
def train(arglist, restore_model_number):
debug = False
multi_process = arglist.mp
num_tasks = arglist.num_task_transfer # 总共有多少个任务
list_of_taskenv = [] # env list
save_path = arglist.save_dir
if not os.path.exists(save_path):
os.makedirs(save_path)
with U.single_threaded_session():
sess = tf.get_default_session()
if debug:
begin = time_begin()
# 1.1创建每个任务的actor trainer和critic trainer
env = make_env(arglist.scenario, reward_type=arglist.reward_type)
env.set_map(
sample_map(arglist.test_data_dir + arglist.test_data_name +
"_1.h5"))
# Create agent trainers
obs_shape_n = [env.observation_space[i].shape for i in range(env.n)]
num_adversaries = min(env.n, arglist.num_adversaries)
actor_0 = get_trainers(env,
"actor_",
num_adversaries,
obs_shape_n,
arglist,
type=0,
session=sess)
# 1.2创建每个任务的actor trainer和critic trainer
critic_list = [] # 所有任务critic的list
actor_list = []
print('Using good policy {} and adv policy {}'.format(
arglist.good_policy, arglist.adv_policy))
# 1.2 全局变量初始化
episodes_rewards = [[0.0] for _ in range(num_tasks)
] # 每个元素为在一个episode中所有agents rewards的和
# agent_rewards[i]中的每个元素记录单个agent在一个episode中所有rewards的和
agent_rewards = [[[0.0] for _ in range(env.n)]
for _ in range(num_tasks)]
final_ep_rewards = [[] for _ in range(num_tasks)
] # sum of rewards for training curve
final_ep_ag_rewards = [[] for _ in range(num_tasks)
] # agent rewards for training curve
energy_consumptions_for_test = [[] for _ in range(num_tasks)]
j_index = [[] for _ in range(num_tasks)]
aver_cover = [[] for _ in range(num_tasks)]
instantaneous_dis = [[] for _ in range(num_tasks)]
instantaneous_out_the_map = [[] for _ in range(num_tasks)]
energy_efficiency = [[] for _ in range(num_tasks)]
instantaneous_accmulated_reward = [[] for _ in range(num_tasks)]
model_number = int(arglist.num_train_episodes / arglist.save_rate)
saver = tf.train.Saver(max_to_keep=model_number)
# 1.3 局部变量初始化
global_steps = np.zeros(num_tasks)
local_steps = np.zeros(num_tasks) # local timesteps for each env
energy_one_episode = [[] for _ in range(num_tasks)]
j_index_one_episode = [[] for _ in range(num_tasks)]
aver_cover_one_episode = [[] for _ in range(num_tasks)]
over_map_counter = np.zeros(num_tasks)
over_map_one_episode = [[] for _ in range(num_tasks)]
disconnected_number_counter = np.zeros(num_tasks)
disconnected_number_one_episode = [[] for _ in range(num_tasks)]
episode_reward_step = np.zeros(
num_tasks) # 累加一个episode里每一步的所有智能体的平均reward
accmulated_reward_one_episode = [[] for _ in range(num_tasks)]
route_one_episode = [[] for _ in range(num_tasks)]
if debug:
print(time_end(begin, "step3"))
begin = time_begin()
# 1.4 加载checkpoints
if arglist.load_dir == "":
arglist.load_dir = os.path.join(save_path,
str(restore_model_number),
"model.ckpt")
if arglist.transfer_restore:
print('Loading previous state...')
U.load_state(arglist.load_dir)
for i in range(num_tasks):
list_of_taskenv.append(
make_env(arglist.scenario, reward_type=arglist.reward_type))
critic_trainers = get_trainers(list_of_taskenv[i],
"task_" + str(i + 1) + "_",
num_adversaries,
obs_shape_n,
arglist,
actors=actor_0,
type=1,
session=sess)
actor_trainers = get_trainers(list_of_taskenv[i],
"task_" + str(i + 1) + "_",
num_adversaries,
obs_shape_n,
arglist,
actor_env_name="actor_",
type=2,
session=sess)
actor_list.append(actor_trainers)
critic_list.append(critic_trainers)
# Initialize
U.initialize()
for var in tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES):
print(var)
# 1.5 初始化ENV
obs_n_list = []
for i in range(num_tasks):
obs_n = list_of_taskenv[i].reset()
list_of_taskenv[i].set_map(
sample_map(arglist.test_data_dir + arglist.test_data_name +
"_" + str(i + 1) + ".h5"))
obs_n_list.append(obs_n)
if debug:
print(time_end(begin, "initialize"))
begin = time_begin()
# 2.训练
t_start = time.time()
print('Starting iterations...')
episode_start_time = time.time()
state_dim = obs_shape_n[0][0]
history_n = [[
queue.Queue(arglist.history_length) for _ in range(env.n)
] for _ in range(num_tasks)]
for i in range(num_tasks):
for j in range(env.n):
for _ in range(arglist.history_length):
history_n[i][j].put(obs_n_list[i][j])
while True:
for task_index in range(num_tasks):
# 2.1更新环境，采集样本
current_env = list_of_taskenv[task_index]
# get action
# action_n = [agent.action(obs) for agent, obs in zip(actor_0, obs_n_list[task_index])]
action_n = [
agent.action(obs)
for agent, obs in zip(actor_0, history_n[task_index])
]
# environment step
new_obs_n, rew_n, done_n, info_n = current_env.step(action_n)
current_critics = critic_list[task_index]
current_actors = actor_list[task_index]
if debug:
print(time_end(begin, "env.step"))
begin = time_begin()
local_steps[task_index] += 1 # 更新局部计数器
global_steps[task_index] += 1 # 更新全局计数器
done = all(done_n)
terminal = (local_steps[task_index] >= arglist.max_episode_len)
# 收集experience
for i in range(env.n):
current_critics[i].experience(obs_n_list[task_index][i],
action_n[i], rew_n[i],
new_obs_n[i], done_n[i],
terminal)
# 更新obs
obs_n_list[task_index] = new_obs_n
for i in range(env.n):
history_n[task_index][i].get()
history_n[task_index][i].put(new_obs_n[i])
# 更新reward
for i, rew in enumerate(rew_n):
episodes_rewards[task_index][-1] += rew
agent_rewards[task_index][i][-1] += rew
# 2.2，优化每一个任务的critic and acotr
for critic in current_critics:
critic.preupdate()
for critic in current_critics:
critic.update(current_critics, global_steps[task_index])
for index, actor in enumerate(current_actors):
actor.update(current_actors, current_critics,
global_steps[task_index], index)
if debug:
print(time_end(begin, "update actor"))
begin = time_begin()
# 2.4 记录和更新train信息
# energy
energy_one_episode[task_index].append(current_env.get_energy())
# fair index
j_index_one_episode[task_index].append(
current_env.get_jain_index())
# coverage
aver_cover_one_episode[task_index].append(
current_env.get_aver_cover())
# over map counter
over_map_counter[task_index] += current_env.get_over_map()
over_map_one_episode[task_index].append(
over_map_counter[task_index])
# disconnected counter
disconnected_number_counter[task_index] += current_env.get_dis(
)
disconnected_number_one_episode[task_index].append(
disconnected_number_counter[task_index])
# reward
episode_reward_step[task_index] += np.mean(rew_n)
accmulated_reward_one_episode[task_index].append(
episode_reward_step[task_index])
route = current_env.get_agent_pos()
route_one_episode[task_index].append(route)
if debug:
print(time_end(begin, "others"))
begin = time_begin()
episode_number = math.ceil(global_steps[task_index] /
arglist.max_episode_len)
if done or terminal:
model_name = save_path.split('/')[-2] + '/'
temp_efficiency = np.array(
aver_cover_one_episode[task_index]) * np.array(
j_index_one_episode[task_index]) / np.array(
energy_one_episode[task_index])
draw_util.draw_single_episode(
arglist.pictures_dir_transfer_train + model_name +
"single_episode_task_" + str(task_index) + "/",
episode_number, temp_efficiency,
aver_cover_one_episode[task_index],
j_index_one_episode[task_index],
energy_one_episode[task_index],
disconnected_number_one_episode[task_index],
over_map_one_episode[task_index],
accmulated_reward_one_episode[task_index])
# 记录每个episode的变量
energy_consumptions_for_test[task_index].append(
energy_one_episode[task_index][-1]) # energy
j_index[task_index].append(
j_index_one_episode[task_index][-1]) # fairness index
aver_cover[task_index].append(
aver_cover_one_episode[task_index][-1]) # coverage
instantaneous_dis[task_index].append(
disconnected_number_one_episode[task_index]
[-1]) # disconnected
instantaneous_out_the_map[task_index].append(
over_map_one_episode[task_index][-1]) # out of the map
instantaneous_accmulated_reward[task_index].append(
accmulated_reward_one_episode[task_index]
[-1]) # reward
energy_efficiency[task_index].append(
aver_cover_one_episode[task_index][-1] *
j_index_one_episode[task_index][-1] /
energy_one_episode[task_index][-1]) # efficiency
episode_end_time = time.time()
episode_time = episode_end_time - episode_start_time
episode_start_time = episode_end_time
with open(
arglist.pictures_dir_transfer_train + model_name +
"task_" + str(task_index) + '_train_info' + '.txt',
'a+') as f:
info = "Task index: %d, Episode number %d, energy consumption: %s, efficiency: %s, time: %s" % (
task_index, episode_number,
str(current_env.get_energy_origin()),
str(energy_efficiency[task_index][-1]),
str(round(episode_time, 3)))
f.write(info + "\n")
print(info)
# 应该在每个重置每个episode中的局部变量--------------------------------------------
if task_index == num_tasks - 1:
energy_one_episode = [[] for _ in range(num_tasks)]
j_index_one_episode = [[] for _ in range(num_tasks)]
aver_cover_one_episode = [[] for _ in range(num_tasks)]
over_map_counter = np.zeros(num_tasks)
over_map_one_episode = [[] for _ in range(num_tasks)]
disconnected_number_counter = np.zeros(num_tasks)
disconnected_number_one_episode = [
[] for _ in range(num_tasks)
]
episode_reward_step = np.zeros(num_tasks)
accmulated_reward_one_episode = [
[] for _ in range(num_tasks)
]
route_one_episode = [[] for _ in range(num_tasks)]
# 重置局部变量
obs_n_list[task_index] = current_env.reset() # 重置env
current_env.set_map(
sample_map(arglist.test_data_dir +
arglist.test_data_name + "_" +
str(task_index + 1) + ".h5"))
local_steps[task_index] = 0 # 重置局部计数器
# 更新全局变量
episodes_rewards[task_index].append(0) # 添加新的元素
for reward in agent_rewards[task_index]:
reward.append(0)
# save model, display training output
if terminal and (episode_number % arglist.save_rate == 0):
# tf.get_default_session().run(global_steps_assign_op, feed_dict={global_steps_ph: global_steps})
# save_dir_custom = os.path.join(save_path, str(episode_number), 'model.ckpt')
# U.save_state(save_dir_custom, saver=saver)
# print statement depends on whether or not there are adversaries
# 最新save_rate个episode的平均reward
save_rate_mean_reward = np.mean(
episodes_rewards[task_index][-arglist.save_rate:])
if num_adversaries == 0:
print(
"steps: {}, episodes: {}, mean episode reward: {}, time: {}"
.format(global_steps[task_index], episode_number,
save_rate_mean_reward,
round(time.time() - t_start, 3)))
else:
print(
"steps: {}, episodes: {}, mean episode reward: {}, agent episode reward: {}, time: {}"
.format(global_steps[task_index], episode_number,
save_rate_mean_reward, [
np.mean(rew[-arglist.save_rate:])
for rew in agent_rewards[task_index]
], round(time.time() - t_start, 3)))
t_start = time.time()
final_ep_rewards[task_index].append(save_rate_mean_reward)
for rew in agent_rewards[task_index]:
final_ep_ag_rewards[task_index].append(
np.mean(rew[-arglist.save_rate:]))
# 保存train曲线
if arglist.draw_picture_train:
# model_name = save_path.split('/')[-2] + '/'
draw_util.draw_episodes(
episode_number,
arglist.pictures_dir_transfer_train + model_name +
"all_episodes_task_" + str(task_index) + "/",
aver_cover[task_index], j_index[task_index],
energy_consumptions_for_test[task_index],
instantaneous_dis[task_index],
instantaneous_out_the_map[task_index],
energy_efficiency[task_index],
instantaneous_accmulated_reward[task_index],
len(aver_cover[task_index]))
# saves final episode reward for plotting training curve later
if episode_number > arglist.num_train_episodes:
mkdir(arglist.plots_dir)
rew_file_name = arglist.plots_dir + arglist.exp_name + str(
task_index) + '_rewards.pkl'
with open(rew_file_name, 'wb') as fp:
pickle.dump(final_ep_rewards, fp)
agrew_file_name = arglist.plots_dir + arglist.exp_name + str(
task_index) + '_agrewards.pkl'
with open(agrew_file_name, 'wb') as fp:
pickle.dump(final_ep_ag_rewards, fp)
print('...Finished total of {} episodes.'.format(
episode_number))
if episode_number > arglist.num_train_episodes:
break
def random_maddpg_test(arglist):
debug = False
num_tasks = arglist.num_task_transfer # 总共有多少个任务
list_of_taskenv = [] # env list
graph = tf.Graph()
with graph.as_default():
with U.single_threaded_session():
if debug:
begin = time_begin()
# 1.1创建common actor
env = make_env(arglist.scenario, reward_type=arglist.reward_type)
env.set_map(sample_map(arglist.test_data_dir + arglist.test_data_name + "_1.h5"))
# Create agent trainers
obs_shape_n = [env.observation_space[i].shape for i in range(env.n)]
num_adversaries = min(env.n, arglist.num_adversaries)
actors = get_trainers(env, "actor_", num_adversaries, obs_shape_n, arglist, type=0)
for i in range(num_tasks):
list_of_taskenv.append(make_env(arglist.scenario, reward_type=arglist.reward_type))
print('Using good policy {} and adv policy {}'.format(arglist.good_policy, arglist.adv_policy))
# 1.2 Initialize
U.initialize()
model_name = arglist.load_dir.split('/')[-2] + '/'
path = arglist.pictures_dir_transfer_test + model_name
mkdir(path)
for i in range(num_tasks):
mkdir(os.path.join(path, "task_" + str(i)))
# 2.1 加载checkpoints
# model_load_dir = os.path.join(arglist.load_dir, str(model_number * arglist.save_rate), 'model.ckpt')
# print('From ', model_load_dir, ' Loading previous state...')
# U.load_state(model_load_dir)
# 3.1 全局变量初始化
global_steps = np.zeros(num_tasks) # global timesteps for each env
episodes_rewards = [[0.0] for _ in range(num_tasks)] # 每个元素为在一个episode中所有agents rewards的和
# agent_rewards[i]中的每个元素记录单个agent在一个episode中所有rewards的和
agent_rewards = [[[0.0] for _ in range(env.n)] for _ in range(num_tasks)]
energy_consumptions_for_test = [[] for _ in range(num_tasks)]
j_index = [[] for _ in range(num_tasks)]
aver_cover = [[] for _ in range(num_tasks)]
instantaneous_dis = [[] for _ in range(num_tasks)]
instantaneous_out_the_map = [[] for _ in range(num_tasks)]
energy_efficiency = [[] for _ in range(num_tasks)]
instantaneous_accmulated_reward = [[] for _ in range(num_tasks)]
# 3.2 局部变量初始化
local_steps = np.zeros(num_tasks) # local timesteps for each env
energy_one_episode = [[] for _ in range(num_tasks)]
j_index_one_episode = [[] for _ in range(num_tasks)]
aver_cover_one_episode = [[] for _ in range(num_tasks)]
over_map_counter = np.zeros(num_tasks)
over_map_one_episode = [[] for _ in range(num_tasks)]
disconnected_number_counter = np.zeros(num_tasks)
disconnected_number_one_episode = [[] for _ in range(num_tasks)]
episode_reward_step = np.zeros(num_tasks) # 累加一个episode里每一步的所有智能体的平均reward
accmulated_reward_one_episode = [[] for _ in range(num_tasks)]
route_one_episode = [[] for _ in range(num_tasks)]
bl_coverage = 0.8
bl_jainindex = 0.8
bl_loss = 100
# 3.3 初始化ENV
obs_n_list = []
for i in range(num_tasks):
obs_n = list_of_taskenv[i].reset()
list_of_taskenv[i].set_map(
sample_map(arglist.test_data_dir + arglist.test_data_name + "_" + str(i + 1) + ".h5", random=False))
obs_n_list.append(obs_n)
# 3.4
history_n = [[queue.Queue(arglist.history_length) for _ in range(env.n)] for _ in range(num_tasks)]
for i in range(num_tasks):
for j in range(env.n):
for _ in range(arglist.history_length):
history_n[i][j].put(obs_n_list[i][j])
# 4 test
episode_start_time = time.time()
print('Starting iterations...')
episode_number = 0
while True:
for task_index in range(num_tasks):
# 3.1更新环境
current_env = list_of_taskenv[task_index]
# get action
action_n = [agent.action(obs) for agent, obs in zip(actors, history_n[task_index])]
# environment step
new_obs_n, rew_n, done_n, info_n = current_env.step(action_n)
local_steps[task_index] += 1 # 更新局部计数器
global_steps[task_index] += 1 # 更新全局计数器
done = all(done_n)
terminal = (local_steps[task_index] >= arglist.max_episode_len)
# 更新obs
obs_n_list[task_index] = new_obs_n
# 更新reward
for i, rew in enumerate(rew_n):
episodes_rewards[task_index][-1] += rew
agent_rewards[task_index][i][-1] += rew
# energy
energy_one_episode[task_index].append(current_env.get_energy())
# fair index
j_index_one_episode[task_index].append(current_env.get_jain_index())
# coverage
aver_cover_one_episode[task_index].append(current_env.get_aver_cover())
# over map counter
over_map_counter[task_index] += current_env.get_over_map()
over_map_one_episode[task_index].append(over_map_counter[task_index])
# disconnected counter
disconnected_number_counter[task_index] += current_env.get_dis()
disconnected_number_one_episode[task_index].append(disconnected_number_counter[task_index])
# reward
episode_reward_step[task_index] += np.mean(rew_n)
accmulated_reward_one_episode[task_index].append(episode_reward_step[task_index])
route = current_env.get_agent_pos()
route_one_episode[task_index].append(route)
episode_number = math.ceil(global_steps[task_index] / arglist.max_episode_len)
if done or terminal:
# 记录每个episode的变量
energy_consumptions_for_test[task_index].append(energy_one_episode[task_index][-1]) # energy
j_index[task_index].append(j_index_one_episode[task_index][-1]) # fairness index
aver_cover[task_index].append(aver_cover_one_episode[task_index][-1]) # coverage
instantaneous_dis[task_index].append(
disconnected_number_one_episode[task_index][-1]) # disconnected
instantaneous_out_the_map[task_index].append(
over_map_one_episode[task_index][-1]) # out of the map
instantaneous_accmulated_reward[task_index].append(
accmulated_reward_one_episode[task_index][-1]) # reward
energy_efficiency[task_index].append(aver_cover_one_episode[task_index][-1]
* j_index_one_episode[task_index][-1] /
energy_one_episode[task_index][-1]) # efficiency
episode_end_time = time.time()
episode_time = episode_end_time - episode_start_time
episode_start_time = episode_end_time
print('Task %d, Episode: %d - energy_consumptions: %s, efficiency: %s, time %s' % (
task_index,
episode_number,
str(current_env.get_energy_origin()),
str(energy_efficiency[task_index][-1]),
str(round(episode_time, 3))))
current_path = os.path.join(path, "task_" + str(task_index))
if arglist.draw_picture_test:
file_path = os.path.join(current_path,
"random_model_test.log")
if episode_number == arglist.num_test_episodes:
report = '\nOK===============report=====================\nRadom maddpg Model-testing ' \
+ str(arglist.num_test_episodes) + ' episodes\'s result:' \
+ '\n!!!Max energy efficiency: ' \
+ str(np.max(energy_efficiency[task_index])) \
+ '\n!!!Average energy efficiency:' \
+ str(np.mean(energy_efficiency[task_index])) \
+ '\nAverage average attained coverage: ' \
+ str(np.mean(aver_cover[task_index])) + \
'\nAverage Jaint\'s fairness index: ' \
+ str(np.mean(j_index[task_index])) + \
'\nAverage normalized average energy consumptions:' \
+ str(np.mean(energy_consumptions_for_test[task_index])) \
+ "\n" + "==========================end=============================\n"
draw_util.drawTest("random",
current_path+"random_maddpg",
energy_efficiency[task_index],
energy_consumptions_for_test[task_index],
aver_cover[task_index],
j_index[task_index],
instantaneous_accmulated_reward[task_index],
instantaneous_dis[task_index],
instantaneous_out_the_map[task_index],
len(aver_cover[task_index]),
bl_coverage,
bl_jainindex,
bl_loss,
False)
else:
report = '\nRandom maddpg Model-' \
+ '-episode ' + str(episode_number) + ' result:' \
+ '\n!!!Energy efficiency: ' \
+ str(energy_efficiency[task_index][-1]) \
+ '\nAverage attained coverage: ' \
+ str(aver_cover[task_index][-1]) + \
'\nJaint\'s fairness index: ' \
+ str(j_index[task_index][-1]) + \
'\nnormalized average energy consumptions: ' \
+ str(energy_consumptions_for_test[task_index][-1]) \
+ "\n"
with open(file_path, 'a+') as file:
file.write(report)
# reset custom statistics variabl between episode and epoch------------------------------------
if task_index == num_tasks - 1:
energy_one_episode = [[] for _ in range(num_tasks)]
j_index_one_episode = [[] for _ in range(num_tasks)]
aver_cover_one_episode = [[] for _ in range(num_tasks)]
over_map_counter = np.zeros(num_tasks)
over_map_one_episode = [[] for _ in range(num_tasks)]
disconnected_number_counter = np.zeros(num_tasks)
disconnected_number_one_episode = [[] for _ in range(num_tasks)]
episode_reward_step = np.zeros(num_tasks)
accmulated_reward_one_episode = [[] for _ in range(num_tasks)]
route_one_episode = [[] for _ in range(num_tasks)]
# 重置局部变量
obs_n_list[task_index] = current_env.reset() # 重置env
current_env.set_map(
sample_map(
arglist.test_data_dir + arglist.test_data_name + "_" + str(task_index + 1) + ".h5",
random=False))
local_steps[task_index] = 0 # 重置局部计数器
# 更新全局变量
episodes_rewards[task_index].append(0) # 添加新的元素
for reward in agent_rewards[task_index]:
reward.append(0)
if episode_number > arglist.num_test_episodes:
break
