def main():
"""
Train the agent
"""
# number of different trainings settings
train_num = [1, 2, 3, 4, 5, 6, 7, 8, 9]
# number of each D2D's Real FB
num_feedback_set = [3]
# number of BS output, that is, the number of compressed global information
num_bs_output_set = [12]
gamma_set = [0.05]
batch_set = [512]
# weight for the V2V sum rate
v2v_weight = 1
# weight for the V2I sum rate
v2i_weight = 0.1
num_train_settings = 1
# start training
for train_loop in range(num_train_settings):
# set the current random seed for training
train_seed_sequence = 1001
random.seed(train_seed_sequence)
np.random.seed(train_seed_sequence)
tf.set_random_seed(train_seed_sequence)
# set values for current simulation
curr_RL_Config = RL_Config()
train_show_tra = '-----Start the Number -- ' + str(
train_num[train_loop]) + ' -- training -----!'
print(train_show_tra)
# set key parameters for this train
num_feedback = num_feedback_set[0]
gamma = gamma_set[0]
batch_size = batch_set[0]
num_bs_output = num_bs_output_set[train_loop]
curr_RL_Config.set_train_value(num_feedback, gamma, batch_size,
num_bs_output, v2v_weight, v2i_weight)
# start the Environment
Env = start_env()
# run the training process
[Train_Loss, Reward_Per_Train_Step, Reward_Per_Episode,
Train_Q_mean, Train_Q_max_mean, Orig_Train_Q_mean, Orig_Train_Q_max_mean] \
= run_train(Env, curr_RL_Config)
# save the train results
save_flag = save_train_results(Train_Loss, Reward_Per_Train_Step,
Reward_Per_Episode, Train_Q_mean,
Train_Q_max_mean, Orig_Train_Q_mean,
Orig_Train_Q_max_mean, curr_RL_Config,
Env)
if save_flag:
print('RL Training is finished!')
def main():
"""
Test the trained agent
"""
# set of different testing seeds
test_num = [1]
# set of different D2D FBs
num_feedback_set = [3]
# number of BS outputs, that is, the number of compressed global information
num_bs_output_set = [12, 12, 16, 24]
gamma_set = [0.05]
# set of different batch sizes
batch_set = [512]
# number of different testing seeds
num_test_settings = 1
# number of different BS outputs and FBs
num_BS_Out = 1
# weight for the V2V sum rate
v2v_weight = 1
# weight for the V2I sum rate
v2i_weight = 0.1
# parameter setting for testing
num_test_episodes = 2000
num_test_steps = 1000
opt_flag = False
# start training
for BS_loop in range(num_BS_Out):
# run at different FB
curr_FB = num_feedback_set[0]
curr_BS_Output = num_bs_output_set[BS_loop]
FB_str = '>>>>>>>>>Testing FB = ' + str(curr_FB) + ' -BS Outputs = ' + str(curr_BS_Output) \
+ ' at different random seeds<<<<<<<<<'
print(FB_str)
for test_loop in range(num_test_settings):
# set the current random seed for training
test_seed_sequence = test_num[test_loop]
random.seed(test_seed_sequence)
np.random.seed(test_seed_sequence)
tf.set_random_seed(test_seed_sequence)
# set values for current simulation
curr_RL_Config = RL_Config()
train_show_tra = '----- Start the Number -- ' + str(
test_num[test_loop]) + ' -- Testing -----!'
print(train_show_tra)
# set key parameters for this train
num_feedback = num_feedback_set[0]
gamma = gamma_set[0]
batch_size = batch_set[0]
num_bs_output = num_bs_output_set[BS_loop]
curr_RL_Config.set_train_value(num_feedback, gamma, batch_size,
num_bs_output, v2v_weight,
v2i_weight)
# display the parameters settings for current trained model
curr_RL_Config.display()
# start the Environment
Env = start_env()
# load the trained model
BS_Agent = load_trained_model(Env, curr_RL_Config)
# set key parameters for this testing
curr_RL_Config.set_test_values(num_test_episodes, num_test_steps,
opt_flag, v2v_weight, v2i_weight)
# run the testing process and save the testing results
save_flag = run_test(curr_RL_Config, BS_Agent, test_seed_sequence)
# track the testing process
if save_flag:
print('RL Testing is finished!')
