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run_simulation.py
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run_simulation.py
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# debug field
import os
os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
import argparse
# get argument from user
parser = argparse.ArgumentParser()
parser.add_argument('--store-path', type = str, required = True, help="pretrained model weight path")
args = parser.parse_args()
STORE_PATH = args.store_path
# validate the weight path
if not os.path.isdir(STORE_PATH):
raise NotImplementedError("The provided weight path does not exist!")
# get DRL_ALGO
if 'td3' in STORE_PATH:
DRL_ALGO = 'td3'
else:
DRL_ALGO = 'ddpg'
# reward design
if 'see' in STORE_PATH:
REWARD_DESIGN = 'see'
else:
REWARD_DESIGN = 'ssr'
# seeds and episode number
SEEDS = None
EPISODE_NUM = args.ep_num
project_name = f'trained_uav/{DRL_ALGO}_{REWARD_DESIGN}' if TRAINED_UAV else f'scratch/{DRL_ALGO}_{REWARD_DESIGN}'
# process the argument
assert DRL_ALGO in ['ddpg', 'td3'], "drl must be ['ddpg', 'td3']"
assert REWARD_DESIGN in ['ssr', 'see'], "reward must be ['ssr', 'see']"
if SEEDS is not None:
assert len(SEEDS) in [1, 2] and isinstance(SEEDS[0], int) and isinstance(SEEDS[-1], int), "seeds must be a list of 1 or 2 integer"
if DRL_ALGO == 'td3':
from td3 import Agent
elif DRL_ALGO == 'ddpg':
from ddpg import Agent
import ddpg
from env import MiniSystem
import numpy as np
import math
import time
import torch
# 1 init system model
episode_num = EPISODE_NUM # recommend to be 300
episode_cnt = 0
step_num = 100
project_name = f'trained_uav/{DRL_ALGO}_{REWARD_DESIGN}' if TRAINED_UAV else f'scratch/{DRL_ALGO}_{REWARD_DESIGN}'
system = MiniSystem(
user_num=2,
RIS_ant_num=4,
UAV_ant_num=4,
if_dir_link=1,
if_with_RIS=True,
if_move_users=True,
if_movements=True,
reverse_x_y=(False, False),
if_UAV_pos_state = True,
reward_design = REWARD_DESIGN,
project_name = project_name,
step_num = step_num
)
if_Theta_fixed = False
if_G_fixed = False
if_BS = False
if_robust = True
# 2 init RL Agent
agent_1_param_dic = {}
agent_1_param_dic["alpha"] = 0.0001
agent_1_param_dic["beta"] = 0.001
agent_1_param_dic["input_dims"] = system.get_system_state_dim()
agent_1_param_dic["tau"] = 0.001
agent_1_param_dic["batch_size"] = 64
agent_1_param_dic["n_actions"] = system.get_system_action_dim() - 2
agent_1_param_dic["action_noise_factor"] = 0.1
agent_1_param_dic["memory_max_size"] = int(5/5 * episode_num * step_num) #/2
agent_1_param_dic["agent_name"] = "G_and_Phi"
agent_1_param_dic["layer1_size"] = 800
agent_1_param_dic["layer2_size"] = 600
agent_1_param_dic["layer3_size"] = 512
agent_1_param_dic["layer4_size"] = 256
agent_2_param_dic = {}
agent_2_param_dic["alpha"] = 0.0001
agent_2_param_dic["beta"] = 0.001
agent_2_param_dic["input_dims"] = 3
agent_2_param_dic["tau"] = 0.001
agent_2_param_dic["batch_size"] = 64
agent_2_param_dic["n_actions"] = 2
agent_2_param_dic["action_noise_factor"] = 0.5
agent_2_param_dic["memory_max_size"] = int(5/5 * episode_num * step_num) #/2
agent_2_param_dic["agent_name"] = "UAV"
agent_2_param_dic["layer1_size"] = 400
agent_2_param_dic["layer2_size"] = 300
agent_2_param_dic["layer3_size"] = 256
agent_2_param_dic["layer4_size"] = 128
if SEEDS is not None:
torch.manual_seed(SEEDS[0]) # 1
torch.cuda.manual_seed_all(SEEDS[0]) # 1
agent_1 = Agent(
alpha = agent_1_param_dic["alpha"],
beta = agent_1_param_dic["beta"],
input_dims = [agent_1_param_dic["input_dims"]],
tau = agent_1_param_dic["tau"],
env = system,
batch_size = agent_1_param_dic["batch_size"],
layer1_size=agent_1_param_dic["layer1_size"],
layer2_size=agent_1_param_dic["layer2_size"],
layer3_size=agent_1_param_dic["layer3_size"],
layer4_size=agent_1_param_dic["layer4_size"],
n_actions = agent_1_param_dic["n_actions"],
max_size = agent_1_param_dic["memory_max_size"],
agent_name= agent_1_param_dic["agent_name"]
)
if SEEDS is not None:
torch.manual_seed(SEEDS[-1]) # 2
torch.cuda.manual_seed_all(SEEDS[-1]) # 2
agent_2 = Agent(
alpha = agent_2_param_dic["alpha"],
beta = agent_2_param_dic["beta"],
input_dims = [agent_2_param_dic["input_dims"]],
tau = agent_2_param_dic["tau"],
env = system,
batch_size = agent_2_param_dic["batch_size"],
layer1_size=agent_2_param_dic["layer1_size"],
layer2_size=agent_2_param_dic["layer2_size"],
layer3_size=agent_2_param_dic["layer3_size"],
layer4_size=agent_2_param_dic["layer4_size"],
n_actions = agent_2_param_dic["n_actions"],
max_size = agent_2_param_dic["memory_max_size"],
agent_name= agent_2_param_dic["agent_name"]
)
if TRAINED_UAV:
benchmark = f'data/storage/benchmark/{DRL_ALGO}_{REWARD_DESIGN}_benchmark'
if DRL_ALGO == 'td3':
agent_2.load_models(
load_file_actor = benchmark + '/Actor_UAV_TD3',
load_file_critic_1 = benchmark + '/Critic_1_UAV_TD3',
load_file_critic_2 = benchmark + '/Critic_2_UAV_TD3'
)
elif DRL_ALGO == 'ddpg':
agent_2.load_models(
load_file_actor = benchmark + '/Actor_UAV_ddpg',
load_file_critic = benchmark + '/Critic_UAV_ddpg'
)
meta_dic = {}
print("***********************system information******************************")
print("folder_name: "+str(system.data_manager.store_path))
meta_dic['folder_name'] = system.data_manager.store_path
print("user_num: "+str(system.user_num))
meta_dic['user_num'] = system.user_num
print("if_dir: "+str(system.if_dir_link))
meta_dic['if_dir_link'] = system.if_dir_link
print("if_with_RIS: "+str(system.if_with_RIS))
meta_dic['if_with_RIS'] = system.if_with_RIS
print("if_user_m: "+str(system.if_move_users))
meta_dic['if_move_users'] = system.if_move_users
print("RIS_ant_num: "+str(system.RIS.ant_num))
meta_dic['system_RIS_ant_num'] = system.RIS.ant_num
print("UAV_ant_num: "+str(system.UAV.ant_num))
meta_dic['system_UAV_ant_num'] = system.UAV.ant_num
print("if_movements: "+str(system.if_movements))
meta_dic['system_if_movements'] = system.if_movements
print("reverse_x_y: "+str(system.reverse_x_y))
meta_dic['system_reverse_x_y'] = system.reverse_x_y
print("if_UAV_pos_state:"+str(system.if_UAV_pos_state))
meta_dic['if_UAV_pos_state'] = system.if_UAV_pos_state
print("ep_num: "+str(episode_num))
meta_dic['episode_num'] = episode_num
print("step_num: "+str(step_num))
meta_dic['step_num'] = step_num
print("***********************agent_1 information******************************")
tplt = "{0:{2}^20}\t{1:{2}^20}"
for i in agent_1_param_dic:
parm = agent_1_param_dic[i]
print(tplt.format(i, parm, chr(12288)))
meta_dic["agent_1"] = agent_1_param_dic
print("***********************agent_2 information******************************")
for i in agent_2_param_dic:
parm = agent_2_param_dic[i]
print(tplt.format(i, parm, chr(12288)))
meta_dic["agent_2"] = agent_2_param_dic
system.data_manager.save_meta_data(meta_dic)
print("***********************traning information******************************")
while episode_cnt < episode_num:
# 1 reset the whole system
system.reset()
step_cnt = 0
score_per_ep = 0
# 2 get the initial state
if if_robust:
tmp = system.observe()
#z = np.random.multivariate_normal(np.zeros(2), 0.5*np.eye(2), size=len(tmp)).view(np.complex128)
z = np.random.normal(size=len(tmp))
observersion_1 = list(
np.array(tmp) + 0.6 *1e-7* z
)
else:
observersion_1 = system.observe()
observersion_2 = list(system.UAV.coordinate)
if episode_cnt == 80:
print("break point")
while step_cnt < step_num:
# 1 count num of step in one episode
step_cnt += 1
# judge if pause the whole system
if not system.render_obj.pause:
# 2 choose action acoording to current state
action_1 = agent_1.choose_action(observersion_1, greedy=agent_1_param_dic["action_noise_factor"] * math.pow((1-episode_cnt / episode_num), 2))
action_2 = agent_2.choose_action(observersion_2, greedy=agent_2_param_dic["action_noise_factor"]* math.pow((1-episode_cnt / episode_num), 2))
if if_BS:
action_2[0]=0
action_2[1]=0
if if_Theta_fixed:
action_1[0+2 * system.UAV.ant_num * system.user_num:] = len(action_1[0+2 * system.UAV.ant_num * system.user_num:])*[0]
if if_G_fixed:
action_1[0:0+2 * system.UAV.ant_num * system.user_num]=np.array([-0.0313, -0.9838, 0.3210, 1.0, -0.9786, -0.1448, 0.3518, 0.5813, -1.0, -0.2803, -0.4616, -0.6352, -0.1449, 0.7040, 0.4090, -0.8521]) * math.pow(episode_cnt / episode_num, 2) * 0.7
#action_1[0:0+2 * system.UAV.ant_num * system.user_num]=len(action_1[0:0+2 * system.UAV.ant_num * system.user_num])*[0.5]
# 3 get newstate, reward
if system.if_with_RIS:
new_state_1, reward, done, info = system.step(
action_0=action_2[0],
action_1=action_2[1],
G=action_1[0:0+2 * system.UAV.ant_num * system.user_num],
Phi=action_1[0+2 * system.UAV.ant_num * system.user_num:],
set_pos_x=action_2[0],
set_pos_y=action_2[1]
)
new_state_2 = list(system.UAV.coordinate)
else:
new_state_1, reward, done, info = system.step(
action_0=action_2[0],
action_1=action_2[1],
G=action_1[0:0+2 * system.UAV.ant_num * system.user_num],
set_pos_x=action_2[0],
set_pos_y=action_2[1]
)
new_state_2 = list(system.UAV.coordinate)
score_per_ep += reward
# 4 store state pair into mem pool
agent_1.remember(observersion_1, action_1, reward, new_state_1, int(done))
agent_2.remember(observersion_2, action_2, reward, new_state_2, int(done))
# 5 update DDPG net
agent_1.learn()
if not TRAINED_UAV:
agent_2.learn()
#system.render_obj.render(0.001) # no rendering for faster
observersion_1 = new_state_1
observersion_2 = new_state_2
if done == True:
break
else:
#system.render_obj.render_pause() # no rendering for faster
time.sleep(0.001) #time.sleep(1)
system.data_manager.save_file(episode_cnt=episode_cnt)
system.reset()
print("ep_num: "+str(episode_cnt)+" ep_score: "+str(score_per_ep))
episode_cnt +=1
if episode_cnt % 10 == 0:
agent_1.save_models()
agent_2.save_models()
# save the last model
agent_1.save_models()
agent_2.save_models()