def main():
################ load ###################
actor_path = os.path.abspath(
os.curdir) + '/Generate_Traffic_Flow_MAS_RL/weight/AC_TD2_actor.pkl'
if os.path.exists(actor_path):
actor = Actor(state_size, action_size).to(device)
actor.load_state_dict(torch.load(actor_path))
print('Actor Model loaded')
else:
actor = Actor(state_size, action_size).to(device)
print("Waiting for GAMA...")
################### initialization ########################
reset()
Using_LSTM = False
test = "GAMA"
N_agent = 20
list_hidden = []
count = 0
################## start #########################
state = GAMA_connect(test)
print("Connected")
while True:
if Using_LSTM == False:
state = [
torch.DoubleTensor(elem).reshape(1, state_size).to(device)
for elem in state
]
state = torch.stack(state).to(device).detach()
tensor_cv = generate_img()
tensor_cv = [
torch.from_numpy(np.transpose(elem,
(2, 0, 1))).double().to(device) /
255 for elem in tensor_cv
]
tensor_cv = torch.stack(tensor_cv).to(device).detach()
action, h_state_cv_a, h_state_n_a = actor(state, tensor_cv)
send_to_GAMA([[1, float(action.cpu().numpy() * 10)]])
else:
if len(list_hidden) < N_agent:
state = [
torch.DoubleTensor(elem).reshape(1, state_size).to(device)
for elem in state
]
state = torch.stack(state).to(device).detach()
tensor_cv = generate_img()
tensor_cv = [
torch.from_numpy(np.transpose(
elem, (2, 0, 1))).double().to(device) / 255
for elem in tensor_cv
]
tensor_cv = torch.stack(tensor_cv).to(device).detach()
action, h_state_cv_a, h_state_n_a = actor(state, tensor_cv)
send_to_GAMA([[1, float(action.cpu().numpy() * 10)]])
list_hidden.append(Memory(h_state_cv_a, h_state_n_a))
count += 1
else:
state = [
torch.DoubleTensor(elem).reshape(1, state_size).to(device)
for elem in state
]
state = torch.stack(state).to(device).detach()
tensor_cv = generate_img()
tensor_cv = [
torch.from_numpy(np.transpose(
elem, (2, 0, 1))).double().to(device) / 255
for elem in tensor_cv
]
tensor_cv = torch.stack(tensor_cv).to(device).detach()
action, h_state_cv_a, h_state_n_a = actor(
state, tensor_cv,
list_hidden[count % N_agent].h_state_cv_a,
list_hidden[count % N_agent].h_state_n_a)
send_to_GAMA([[1, float(action.cpu().numpy() * 10)]])
list_hidden[count % N_agent].set_hidden(
h_state_cv_a, h_state_n_a)
count += 1
state = GAMA_connect(test)
return None
def main():
################ load ###################
if os.path.exists(
'D:/Software/PythonWork/GAMA_python/A2C-TD-single-car-intersection/weight/actor.pkl'
):
actor = Actor(state_size, action_size).to(device)
actor.load_state_dict(
torch.load(
'D:/Software/PythonWork/GAMA_python/A2C-TD-single-car-intersection/weight/actor.pkl'
))
print('Actor Model loaded')
else:
actor = Actor(state_size, action_size).to(device)
if os.path.exists(
'D:/Software/PythonWork/GAMA_python/A2C-TD-single-car-intersection/weight/critic.pkl'
):
critic = Critic(state_size, action_size).to(device)
critic.load_state_dict(
torch.load(
'D:/Software/PythonWork/GAMA_python/A2C-TD-single-car-intersection/weight/critic.pkl'
))
print('Critic Model loaded')
else:
critic = Critic(state_size, action_size).to(device)
print("Waiting for GAMA...")
################### initialization ########################
reset()
lr = 0.00007
optimizerA = optim.Adam(actor.parameters(), lr, betas=(0.95, 0.999))
optimizerC = optim.Adam(critic.parameters(), lr, betas=(0.95, 0.999))
episode = 0
test = "GAMA"
state, reward, done, time_pass, over = GAMA_connect(test) #connect
print("done:", done, "timepass:"******"restart acceleration: 0")
send_to_GAMA([[1, 0]])
#先传后计算
rewards.append(reward) #contains the last
reward = torch.tensor([reward], dtype=torch.float, device=device)
rewards.append(reward) #contains the last
total_reward = sum(rewards)
total_rewards.append(total_reward)
#state = torch.FloatTensor(state).reshape(1,4).to(device)
#last_value= critic(state)
with torch.autograd.set_detect_anomaly(True):
advantage = reward.detach(
) - value #+ last_value 最后一回的V(s+1) = 0
actor_loss = -(log_prob * advantage.detach())
print("actor_loss, ", actor_loss, " size", actor_loss.dim())
critic_loss = (reward.detach() - value).pow(2) #+ last_value
lstm_loss = critic_loss
optimizerA.zero_grad()
optimizerC.zero_grad()
critic_loss.backward(retain_graph=True)
actor_loss.backward(retain_graph=True)
loss.append(critic_loss)
optimizerA.step()
optimizerC.step()
print(
"----------------------------------Net_Trained---------------------------------------"
)
print('--------------------------Iteration:', episode,
'over--------------------------------')
episode += 1
log_probs = []
values = []
rewards = []
masks = []
torch.save(
actor.state_dict(),
'D:/Software/PythonWork/GAMA_python/A2C-TD-single-car-intersection/weight/actor.pkl'
)
torch.save(
critic.state_dict(),
'D:/Software/PythonWork/GAMA_python/A2C-TD-single-car-intersection/weight/critic.pkl'
)
loss_sum = sum(loss)
total_loss.append(loss_sum)
cross_loss_curve(total_loss, total_rewards)
loss = []
if episode > 50: #50
lr = 0.0002
if episode > 115:
lr = 0.0001
new_lr = lr * (0.94**((episode - 40) // 10)) #40
optimizerA = optim.Adam(actor.parameters(),
new_lr,
betas=(0.95, 0.999))
optimizerC = optim.Adam(critic.parameters(),
new_lr,
betas=(0.95, 0.999))
#最初の時
else:
print('Iteration:', episode)
state = np.reshape(state, (1, len(state))) #xxx
state = torch.FloatTensor(state).reshape(1, 4).to(device)
value = critic(
state) #dist, # now is a tensoraction = dist.sample()
action, log_prob, entropy = actor(state)
print("acceleration: ", action.cpu().numpy())
send_to_GAMA([[1, float(action.cpu().numpy() * 10)]])
log_prob = log_prob.unsqueeze(0)
entropy += entropy
state, reward, done, time_pass, over = GAMA_connect(test)
return None
def main():
################ load ###################
actor_path = os.path.abspath(
os.curdir) + '/PPO_Mixedinput_Navigation_Model/weight/AC_TD3_actor.pkl'
critic_path = os.path.abspath(
os.curdir
) + '/PPO_Mixedinput_Navigation_Model/weight/AC_TD3_critic.pkl'
if os.path.exists(actor_path):
actor = Actor(state_size, action_size).to(device)
actor.load_state_dict(torch.load(actor_path))
print('Actor Model loaded')
else:
actor = Actor(state_size, action_size).to(device)
if os.path.exists(critic_path):
critic = Critic(state_size, action_size).to(device)
critic.load_state_dict(torch.load(critic_path))
print('Critic Model loaded')
else:
critic = Critic(state_size, action_size).to(device)
critic_next = Critic(state_size, action_size).to(device)
critic_next.load_state_dict(critic.state_dict())
print("Waiting for GAMA...")
################### initialization ########################
reset()
episode = 4000
training_stage = 70 #100#80
Decay = training_stage * 18
lr = 0.0001
sample_lr = [
0.0001, 0.00009, 0.00008, 0.00007, 0.00006, 0.00005, 0.00004, 0.00003,
0.00002, 0.00001, 0.000009, 0.000008, 0.000007, 0.000006, 0.000005,
0.000004, 0.000003, 0.000002, 0.000001
] #900 960 1020 1080 1140
if episode >= training_stage: #50 100
try:
lr = sample_lr[int(episode // training_stage)]
except (IndexError):
lr = 0.000001 * (0.9**((episode - Decay // training_stage))
) #100-1800#80-1440#65-1170#570 -- 30
optimizerA = optim.Adam(actor.parameters(), lr, betas=(0.95, 0.999))
optimizerC = optim.Adam(critic.parameters(), lr, betas=(0.95, 0.999))
test = "GAMA"
state, reward, done, time_pass, over, _ = GAMA_connect(test) #connect
print("done:", done, "timepass:"******"----------------------------------Net_Trained---------------------------------------"
)
print('--------------------------Iteration:', episode,
'over--------------------------------')
episode += 1
#最初の時
else:
print('Iteration:', episode, "lr:", lr)
state = np.reshape(state, (1, len(state))) #xxx
state_img = generate_img()
tensor_cv = torch.from_numpy(np.transpose(
state_img, (2, 0, 1))).double().to(device) / 255
state = torch.DoubleTensor(state).reshape(1, state_size).to(device)
for _ in range(Memory_size):
memory.states.append(state)
memory.states_img.append(tensor_cv)
state = torch.stack(memory.states).to(device).detach() ###
tensor_cv = torch.stack(memory.states_img).to(device).detach()
value, h_state_cv_c, h_state_n_c, h_state_3_c = critic(
state,
tensor_cv) #dist, # now is a tensoraction = dist.sample()
action, log_prob, entropy = actor(
state, tensor_cv) #, h_state_cv_a,h_state_n_a,h_state_3_a
print("acceleration: ", action.cpu().numpy())
send_to_GAMA([[1, float(action.cpu().numpy() * 10)]])
log_prob = log_prob.unsqueeze(0)
#entropy += entropy
state, reward, done, time_pass, over, average_speed_NPC = GAMA_connect(
test)
return None
def main():
############## Hyperparameters ##############
K_epochs = 3 # update policy for K epochs lr太大会出现NAN?
eps_clip = 0.2
gamma = 0.9 # 要较弱；较强关联？ 对每一正确步也有打击
episode = 3
lr_first = 0.00001
lr = lr_first #random_seed = None
state_dim = 6
action_dim = 1
#(self, state_dim, action_dim, lr, betas, gamma, K_epochs, eps_clip)
actor_path = os.getcwd(
) + '\\GAMA_python\\PPO_Mixedinput_Navigation_Model\\weight\\ppo_MC_actor.pkl'
critic_path = os.getcwd(
) + '\\GAMA_python\\PPO_Mixedinput_Navigation_Model\\weight\\ppo_MC_critic.pkl'
################ load ###################
if episode > 30: #50 100
lr_first = 0.00001
lr = lr_first * (0.7**((episode - 20) // 10))
ppo = PPO(state_dim, action_dim, lr, gamma, K_epochs, eps_clip)
if os.path.exists(actor_path):
ppo.actor.load_state_dict(torch.load(actor_path))
print('Actor Model loaded')
if os.path.exists(critic_path):
ppo.critic.load_state_dict(torch.load(critic_path))
print('Critic Model loaded')
print("Waiting for GAMA...")
################### initialization ########################
save_curve_pic = os.getcwd(
) + '\\GAMA_python\\PPO_Mixedinput_Navigation_Model\\result\\PPO_MC_loss_curve.png'
save_critic_loss = os.getcwd(
) + '\\GAMA_python\\PPO_Mixedinput_Navigation_Model\\training_data\\PPO_MC_critic_loss.csv'
save_reward = os.getcwd(
) + '\\GAMA_python\\PPO_Mixedinput_Navigation_Model\\training_data\\PPO_MC_reward.csv'
reset()
memory = Memory()
advantages = 0 #global value
loss = []
total_loss = []
rewards = []
total_rewards = []
test = "GAMA"
state, reward, done, time_pass, over = GAMA_connect(test) #connect
#[real_speed/10, target_speed/10, elapsed_time_ratio, distance_left/100,distance_front_car/10,distance_behind_car/10,reward,done,over]
print("done:", done, "timepass:"******"----------------------------------Net_Trained---------------------------------------"
)
print('--------------------------Iteration:', episode,
'over--------------------------------')
episode += 1
loss_sum = sum(loss).cpu().detach().numpy()
total_loss.append(loss_sum)
total_reward = sum(rewards)
total_rewards.append(total_reward)
cross_loss_curve(loss_sum.squeeze(0), total_reward, save_curve_pic,
save_critic_loss, save_reward)
rewards = []
loss = []
if episode > 30: #50 100
lr = lr_first * (0.94**((episode - 20) // 10))
#if episode > 80:
# lr_first = 0.0001
# lr = lr_first * (0.94 ** ((episode-70) // 10))
torch.save(ppo.actor.state_dict(), actor_path)
torch.save(ppo.critic.state_dict(), critic_path)
#最初の時
else:
print('Iteration:', episode)
state = torch.DoubleTensor(state).reshape(1, 6).to(device)
state_img = generate_img(
) # numpy image: H x W x C (500, 500, 3) -> (3,500,500)
tensor_cv = torch.from_numpy(np.transpose(
state_img, (2, 0, 1))).double().to(
device
) # np.transpose( xxx, (2, 0, 1)) torch image: C x H x W
action = ppo.select_action(state, tensor_cv, memory)
print("acceleration: ", action) #.cpu().numpy()
send_to_GAMA([[1, float(action.cpu().numpy() * 10)]])
state, reward, done, time_pass, over = GAMA_connect(test)
return None
def main():
############## Hyperparameters ##############
update_timestep = 1 #TD use == 1 # update policy every n timesteps set for TD
K_epochs = 4 # update policy for K epochs lr太大会出现NAN?
eps_clip = 0.2
gamma = 0.9
episode = 512
sample_lr = [
0.0001, 0.00009, 0.00008, 0.00007, 0.00006, 0.00005, 0.00004, 0.00003,
0.00002, 0.00001, 0.000009, 0.000008, 0.000007, 0.000006, 0.000005,
0.000004, 0.000003, 0.000002, 0.000001
]
lr = 0.0001 #random_seed = None
state_dim = 5
action_dim = 1
#(self, state_dim, action_dim, lr, betas, gamma, K_epochs, eps_clip)
actor_path = os.getcwd(
) + '/PPO_Mixedinput_Navigation_Model/weight/ppo_TD2lstm_actor.pkl'
critic_path = os.getcwd(
) + '/PPO_Mixedinput_Navigation_Model/weight/ppo_TD2lstm_critic.pkl'
################ load ###################
if episode > 50: #50 100
try:
lr = sample_lr[int(episode // 50)]
except (IndexError):
lr = 0.000001
ppo = PPO(state_dim, action_dim, lr, gamma, K_epochs, eps_clip)
if os.path.exists(actor_path):
ppo.actor.load_state_dict(torch.load(actor_path))
print('Actor Model loaded')
if os.path.exists(critic_path):
ppo.critic.load_state_dict(torch.load(critic_path))
print('Critic Model loaded')
print("Waiting for GAMA...")
################### initialization ########################
save_curve_pic = os.getcwd(
) + '/PPO_Mixedinput_Navigation_Model/result/PPO_2LSTM_loss_curve.png'
save_critic_loss = os.getcwd(
) + '/PPO_Mixedinput_Navigation_Model/training_data/PPO_TD2_critic_loss.csv'
save_reward = os.getcwd(
) + '/PPO_Mixedinput_Navigation_Model/training_data/PPO_TD2_reward.csv'
reset()
memory = Memory()
advantages = 0 #global value
loss = []
total_loss = []
rewards = []
total_rewards = []
test = "GAMA"
state, reward, done, time_pass, over = GAMA_connect(test) #connect
#[real_speed/10, target_speed/10, elapsed_time_ratio, distance_left/100,distance_front_car/10,distance_behind_car/10,reward,done,over]
print("done:", done, "timepass:"******"state ",state)
rewards.append(reward)
memory.rewards.append(reward)
memory.is_terminals.append(done)
state = torch.DoubleTensor(state).reshape(1, state_dim).to(device)
state_img = generate_img()
tensor_cv = torch.from_numpy(np.transpose(
state_img, (2, 0, 1))).double().to(device)
if len(memory.states_next) == 0:
for _ in range(3):
memory.states_next = memory.states
memory.states_next[2] = state
memory.states_img_next = memory.states_img
memory.states_img_next[2] = tensor_cv
else:
del memory.states_next[:1]
del memory.states_img_next[:1]
memory.states_next.append(state)
memory.states_img_next.append(tensor_cv)
loss_ = ppo.update(memory, lr, advantages, done)
loss.append(loss_)
del memory.logprobs[:]
del memory.rewards[:]
del memory.is_terminals[:]
#memory.clear_memory()
action = ppo.select_action(state, tensor_cv, memory)
send_to_GAMA([[1, float(action * 10)]])
#print("acceleration ",float(action))
# 終わり
elif done == 1:
#先传后计算
print("state_last", state)
send_to_GAMA([[1, 0]])
rewards.append(reward)
del memory.states_next[:1]
del memory.states_img_next[:1]
state = torch.DoubleTensor(state).reshape(1, state_dim).to(
device) #转化成1行
memory.states_next.append(state)
state_img = generate_img()
tensor_cv = torch.from_numpy(np.transpose(
state_img, (2, 0, 1))).double().to(device)
memory.states_img_next.append(tensor_cv)
memory.rewards.append(reward)
memory.is_terminals.append(done)
loss_ = ppo.update(memory, lr, advantages, done)
loss.append(loss_)
memory.clear_memory()
print(
"----------------------------------Net_Trained---------------------------------------"
)
print('--------------------------Iteration:', episode,
'over--------------------------------')
episode += 1
loss_sum = sum(loss).cpu().detach().numpy()
total_loss.append(loss_sum)
total_reward = sum(rewards)
total_rewards.append(total_reward)
cross_loss_curve(loss_sum.squeeze(0), total_reward, save_curve_pic,
save_critic_loss, save_reward)
rewards = []
loss = []
if episode > 50: #50 100
try:
lr = sample_lr[int(episode // 50)]
except (IndexError):
lr = 0.000001
torch.save(ppo.actor.state_dict(), actor_path)
torch.save(ppo.critic.state_dict(), critic_path)
#最初の時
else:
print('Iteration:', episode, "lr:", lr)
state = torch.DoubleTensor(state).reshape(1, state_dim).to(device)
state_img = generate_img(
) # numpy image: H x W x C (500, 500, 3) -> (3,500,500)
tensor_cv = torch.from_numpy(np.transpose(
state_img, (2, 0, 1))).double().to(
device
) # np.transpose( xxx, (2, 0, 1)) torch image: C x H x W
action = ppo.select_action(state, tensor_cv, memory)
print("acceleration: ", action)
send_to_GAMA([[1, float(action * 10)]])
state, reward, done, time_pass, over = GAMA_connect(test)
return None
def main():
################ load ###################
#train_agent
actor_train_path = os.path.abspath(
os.curdir) + '/Generate_Traffic_Flow_MAS_RL/weight/AC_TD3_actor.pkl'
critic_train_path = os.path.abspath(
os.curdir) + '/Generate_Traffic_Flow_MAS_RL/weight/AC_TD3_critic.pkl'
if os.path.exists(actor_train_path):
actor_train = Actor(state_size, action_size).to(device)
actor_train.load_state_dict(torch.load(actor_train_path))
print('Actor_Train Model loaded')
else:
actor_train = Actor(state_size, action_size).to(device)
if os.path.exists(critic_train_path):
critic_train = Critic(state_size, action_size).to(device)
critic_train.load_state_dict(torch.load(critic_train_path))
print('Critic_Train Model loaded')
else:
critic_train = Critic(state_size, action_size).to(device)
critic_next_train = Critic(state_size, action_size).to(device)
critic_next_train.load_state_dict(critic_train.state_dict())
#agents
actor_path = os.path.abspath(
os.curdir) + '/Generate_Traffic_Flow_MAS_RL/weight/AC_TD_MAS_actor.pkl'
if os.path.exists(actor_path):
actor = Actor(state_size, action_size).to(device)
actor.load_state_dict(torch.load(actor_path))
print('Actor Model loaded')
print("Waiting for GAMA...")
################### initialization ########################
reset()
episode = 0
training_stage = 65
lr = 0.0001
sample_lr = [
0.0001, 0.00009, 0.00008, 0.00007, 0.00006, 0.00005, 0.00004, 0.00003,
0.00002, 0.00001, 0.000009, 0.000008, 0.000007, 0.000006, 0.000005,
0.000004, 0.000003, 0.000002, 0.000001
]
if episode > training_stage: #50 100
try:
lr = sample_lr[int(episode // training_stage)] * 0.01
except (IndexError):
lr = 0.000001 * 0.9 #* (0.9 ** ((episode-1000) // 60))
optimizerA = optim.Adam(actor_train.parameters(), lr, betas=(0.95, 0.999))
optimizerC = optim.Adam(critic_train.parameters(), lr, betas=(0.95, 0.999))
values = []
rewards = []
masks = []
total_loss = []
total_rewards = []
loss = []
average_speed = []
value = 0
gama = 0.9
over = 0
log_prob = 0
memory = Memory()
A_T, state, reward, done, time_pass, over, average_speed_NPC = GAMA_connect(
)
print("Connected")
################## start #########################
while over != 1:
#training_agent
if A_T == 0:
#普通の場合
average_speed.append(state[0])
if (done == 0 and time_pass != 0):
#前回の報酬
reward = torch.tensor([reward],
dtype=torch.float,
device=device)
rewards.append(reward)
state = torch.DoubleTensor(state).reshape(
1, state_size).to(device)
state_img = generate_img_train()
tensor_cv = torch.from_numpy(np.transpose(
state_img, (2, 0, 1))).double().to(device) / 255
if len(memory.states_next) == 0:
#for _ in range(3):
memory.states_next = memory.states
memory.states_next[2] = state
memory.states_img_next = memory.states_img
memory.states_img_next[2] = tensor_cv
else:
del memory.states_next[:1]
del memory.states_img_next[:1]
memory.states_next.append(state)
memory.states_img_next.append(tensor_cv)
state_next = torch.stack(
memory.states_next).to(device).detach()
tensor_cv_next = torch.stack(
memory.states_img_next).to(device).detach()
value_next, _, _, _ = critic_next_train(
state_next, tensor_cv_next, h_state_cv_c, h_state_n_c,
h_state_3_c) #_next
with torch.autograd.set_detect_anomaly(True):
# TD:r(s) + gama*v(s+1) - v(s)
advantage = reward.detach(
) + gama * value_next.detach() - value
actor_loss = -(log_prob * advantage.detach())
critic_loss = (reward.detach() +
gama * value_next.detach() - value).pow(2)
optimizerA.zero_grad()
optimizerC.zero_grad()
critic_loss.backward()
actor_loss.backward()
loss.append(critic_loss)
optimizerA.step()
optimizerC.step()
critic_next_train.load_state_dict(
critic_train.state_dict())
del memory.states[:1]
del memory.states_img[:1]
memory.states.append(state)
memory.states_img.append(tensor_cv)
state = torch.stack(memory.states).to(device).detach()
tensor_cv = torch.stack(memory.states_img).to(device).detach()
value, h_state_cv_c, h_state_n_c, h_state_3_c = critic_train(
state, tensor_cv, h_state_cv_c, h_state_n_c, h_state_3_c)
action, log_prob = actor_train(state, tensor_cv)
log_prob = log_prob.unsqueeze(0)
send_to_GAMA([[1, float(action.cpu().numpy() * 10)]]) #行
masks.append(
torch.tensor([1 - done], dtype=torch.float, device=device))
values.append(value)
# 終わり
elif done == 1:
average_speed.append(state[0])
send_to_GAMA([[1, 0]])
#先传后计算
print(state)
rewards.append(reward) #contains the last
reward = torch.tensor([reward],
dtype=torch.float,
device=device)
rewards.append(reward) #contains the last
total_reward = sum(rewards).cpu().detach().numpy()
total_rewards.append(total_reward)
with torch.autograd.set_detect_anomaly(True):
advantage = reward.detach(
) - value #+ last_value 最后一回的V(s+1) = 0
actor_loss = -(log_prob * advantage.detach())
critic_loss = (reward.detach() - value).pow(
2) #+ last_value
optimizerA.zero_grad()
optimizerC.zero_grad()
critic_loss.backward()
actor_loss.backward()
loss.append(critic_loss)
optimizerA.step()
optimizerC.step()
critic_next_train.load_state_dict(
critic_train.state_dict())
print(
"----------------------------------Net_Trained---------------------------------------"
)
print('--------------------------Iteration:', episode,
'over--------------------------------')
episode += 1
values = []
rewards = []
loss_sum = sum(loss).cpu().detach().numpy()
total_loss.append(loss_sum)
cross_loss_curve(loss_sum.squeeze(0), total_reward,
save_curve_pic, save_critic_loss, save_reward,
np.mean(average_speed), save_speed,
average_speed_NPC, save_NPC_speed)
#total_loss,total_rewards#np.mean(average_speed)/10
loss = []
average_speed = []
memory.clear_memory()
torch.save(actor_train.state_dict(), actor_train_path)
torch.save(critic_train.state_dict(), critic_train_path)
if episode > training_stage: #50 100
try:
lr = sample_lr[int(episode // training_stage)] * 0.01
except (IndexError):
lr = 0.000001 * 0.9 #* (0.9 ** ((episode-1000) // 60))
optimizerA = optim.Adam(actor_train.parameters(),
lr,
betas=(0.95, 0.999))
optimizerC = optim.Adam(critic_train.parameters(),
lr,
betas=(0.95, 0.999))
#最初の時
if time_pass == 0:
print('Iteration:', episode, "lr:", lr)
state = np.reshape(state, (1, len(state)))
state_img = generate_img_train()
tensor_cv = torch.from_numpy(np.transpose(
state_img, (2, 0, 1))).double().to(device) / 255
state = torch.DoubleTensor(state).reshape(
1, state_size).to(device)
for _ in range(3):
memory.states.append(state)
memory.states_img.append(tensor_cv)
state = torch.stack(memory.states).to(device).detach() ###
tensor_cv = torch.stack(memory.states_img).to(device).detach()
value, h_state_cv_c, h_state_n_c, h_state_3_c = critic_train(
state,
tensor_cv) #dist, # now is a tensoraction = dist.sample()
action, log_prob = actor_train(state, tensor_cv)
print("acceleration: ", action.cpu().numpy())
send_to_GAMA([[1, float(action.cpu().numpy() * 10)]])
#agents
if A_T == 1:
state = [
torch.DoubleTensor(elem).reshape(1, state_size).to(device)
for elem in state
]
state = torch.stack(state).to(device).detach()
tensor_cv_MAS = generate_img()
tensor_cv_MAS = [
torch.from_numpy(np.transpose(elem,
(2, 0, 1))).double().to(device) /
255 for elem in tensor_cv_MAS
]
tensor_cv_MAS = torch.stack(tensor_cv_MAS).to(device).detach()
action, _ = actor(state, tensor_cv_MAS)
send_to_GAMA([[1, float(action.cpu().numpy() * 10)]])
A_T, state, reward, done, time_pass, over, average_speed_NPC = GAMA_connect(
)
return None
def main():
############## Hyperparameters ##############
update_timestep = 1 #TD use == 1 # update policy every n timesteps set for TD
K_epochs = 2 # update policy for K epochs lr太大会出现NAN?
eps_clip = 0.2
gamma = 0.9
episode = 376
lr_first = 0.0001
lr = lr_first #random_seed = None
state_dim = 6
action_dim = 1
#(self, state_dim, action_dim, lr, betas, gamma, K_epochs, eps_clip)
actor_path = os.getcwd(
) + '\\GAMA_python\\PPO_Navigation_Model\\weight\\ppo_TD_actor.pkl'
critic_path = os.getcwd(
) + '\\GAMA_python\\PPO_Navigation_Model\\weight\\ppo_TD_critic.pkl'
################ load ###################
if episode > 70: #50 100
lr_first = 0.00001
lr = lr_first * (0.65**((episode - 60) // 10))
ppo = PPO(state_dim, action_dim, lr, gamma, K_epochs, eps_clip)
if os.path.exists(actor_path):
ppo.actor.load_state_dict(torch.load(actor_path))
print('Actor Model loaded')
if os.path.exists(critic_path):
ppo.critic.load_state_dict(torch.load(critic_path))
print('Critic Model loaded')
print("Waiting for GAMA...")
################### initialization ########################
save_curve_pic = os.getcwd(
) + '\\GAMA_python\\PPO_Navigation_Model\\result\\PPO_TD_loss_curve.png'
save_critic_loss = os.getcwd(
) + '\\GAMA_python\\PPO_Navigation_Model\\training_data\\PPO_TD_critic_loss.csv'
save_reward = os.getcwd(
) + '\\GAMA_python\\PPO_Navigation_Model\\training_data\\PPO_TD_reward.csv'
reset()
memory = Memory()
advantages = 0 #global value
loss = []
total_loss = []
rewards = []
total_rewards = []
test = "GAMA"
state, reward, done, time_pass, over = GAMA_connect(test) #connect
#[real_speed/10, target_speed/10, elapsed_time_ratio, distance_left/100,distance_front_car/10,distance_behind_car/10,reward,done,over]
print("done:", done, "timepass:"******"state_last", state)
send_to_GAMA([[1, 0]])
rewards.append(reward)
state = torch.DoubleTensor(state).reshape(1, 6).to(device) #转化成1行
memory.states_next.append(state)
memory.rewards.append(reward)
memory.is_terminals.append(done)
loss_ = ppo.update(memory, lr, advantages, done)
loss.append(loss_)
memory.clear_memory()
print(
"----------------------------------Net_Trained---------------------------------------"
)
print('--------------------------Iteration:', episode,
'over--------------------------------')
episode += 1
loss_sum = sum(loss).cpu().detach().numpy()
total_loss.append(loss_sum)
total_reward = sum(rewards)
total_rewards.append(total_reward)
cross_loss_curve(loss_sum.squeeze(0), total_reward, save_curve_pic,
save_critic_loss, save_reward)
rewards = []
loss = []
if episode > 70: #50 100
lr_first = 0.00001
lr = lr_first * (0.65**((episode - 60) // 10)) #40 90
torch.save(ppo.actor.state_dict(), actor_path)
torch.save(ppo.critic.state_dict(), critic_path)
#最初の時
else:
print('Iteration:', episode)
state = torch.DoubleTensor(state).reshape(1, 6).to(device)
action = ppo.select_action(state, memory)
print("acceleration: ", action) #.cpu().numpy()
send_to_GAMA([[1, float(action * 10)]])
state, reward, done, time_pass, over = GAMA_connect(test)
return None
def main():
################ load ###################
actor_path = os.path.abspath(
os.curdir) + '/PPO_Mixedinput_Navigation_Model/weight/AC_TD0_actor.pkl'
critic_path = os.path.abspath(
os.curdir
) + '/PPO_Mixedinput_Navigation_Model/weight/AC_TD0_critic.pkl'
if os.path.exists(actor_path):
actor = Actor(state_size, action_size).to(device)
actor.load_state_dict(torch.load(actor_path))
print('Actor Model loaded')
else:
actor = Actor(state_size, action_size).to(device)
if os.path.exists(critic_path):
critic = Critic(state_size, action_size).to(device)
critic.load_state_dict(torch.load(critic_path))
print('Critic Model loaded')
else:
critic = Critic(state_size, action_size).to(device)
critic_next = Critic(state_size, action_size).to(device)
critic_next.load_state_dict(critic.state_dict())
print("Waiting for GAMA...")
################### initialization ########################
reset()
episode = 1257
lr = 0.0001
sample_lr = [
0.0001, 0.00009, 0.00008, 0.00007, 0.00006, 0.00005, 0.00004, 0.00003,
0.00002, 0.00001, 0.000009, 0.000008, 0.000007, 0.000006, 0.000005,
0.000004, 0.000003, 0.000002, 0.000001
]
if episode > 50: #50 100
try:
lr = sample_lr[int(episode // 50)]
except (IndexError):
lr = 0.000001
optimizerA = optim.Adam(actor.parameters(), lr, betas=(0.95, 0.999))
optimizerC = optim.Adam(critic.parameters(), lr, betas=(0.95, 0.999))
test = "GAMA"
state, reward, done, time_pass, over = GAMA_connect(test) #connect
print("done:", done, "timepass:"******"----------------------------------Net_Trained---------------------------------------"
)
print('--------------------------Iteration:', episode,
'over--------------------------------')
episode += 1
log_probs = []
values = []
rewards = []
masks = []
loss_sum = sum(loss).cpu().detach().numpy()
total_loss.append(loss_sum)
cross_loss_curve(loss_sum.squeeze(0), total_reward, save_curve_pic,
save_critic_loss,
save_reward) #total_loss,total_rewards
loss = []
memory.clear_memory()
if episode > 50: #50 100
try:
lr = sample_lr[int(episode // 50)]
except (IndexError):
lr = 0.000001
optimizerA = optim.Adam(actor.parameters(),
lr,
betas=(0.95, 0.999))
optimizerC = optim.Adam(critic.parameters(),
lr,
betas=(0.95, 0.999))
torch.save(actor.state_dict(), actor_path)
torch.save(critic.state_dict(), critic_path)
#最初の時
else:
print('Iteration:', episode, "lr:", lr)
state = np.reshape(state, (1, len(state))) #xxx
state_img = generate_img()
tensor_cv = torch.from_numpy(np.transpose(
state_img, (2, 0, 1))).double().to(device)
state = torch.DoubleTensor(state).reshape(1, state_size).to(device)
for _ in range(3):
memory.states.append(state)
memory.states_img.append(tensor_cv)
state = torch.stack(memory.states).to(device).detach() ###
tensor_cv = torch.stack(memory.states_img).to(device).detach()
value = critic(
state,
tensor_cv) #dist, # now is a tensoraction = dist.sample()
action, log_prob, entropy = actor(state, tensor_cv)
print("acceleration: ", action.cpu().numpy())
send_to_GAMA([[1, float(action.cpu().numpy() * 10)]])
log_prob = log_prob.unsqueeze(0)
entropy += entropy
state, reward, done, time_pass, over = GAMA_connect(test)
return None
def main():
################ load ###################
actor_path = os.path.abspath(
os.curdir
) + '\\GAMA_python\\PPO_Mixedinput_Navigation_Model\\weight\\AC_TD_actor.pkl'
critic_path = os.path.abspath(
os.curdir
) + '\\GAMA_python\\PPO_Mixedinput_Navigation_Model\\weight\\AC_TD_critic.pkl'
if os.path.exists(actor_path):
actor = Actor(state_size, action_size).to(device)
actor.load_state_dict(torch.load(actor_path))
print('Actor Model loaded')
else:
actor = Actor(state_size, action_size).to(device)
if os.path.exists(critic_path):
critic = Critic(state_size, action_size).to(device)
critic.load_state_dict(torch.load(critic_path))
print('Critic Model loaded')
else:
critic = Critic(state_size, action_size).to(device)
critic_next = Critic(state_size, action_size).to(device)
critic_next.load_state_dict(critic.state_dict())
print("Waiting for GAMA...")
################### initialization ########################
reset()
episode = 237
lr = 0.0001
if episode > 50:
lr = 0.00008
new_lr = lr * (0.9**((episode - 40) // 10))
#if episode > 110:
# lr = 0.000008
# new_lr = lr * (0.9 ** ((episode-90) // 10)) #40
optimizerA = optim.Adam(actor.parameters(), lr, betas=(0.95, 0.999))
optimizerC = optim.Adam(critic.parameters(), lr, betas=(0.95, 0.999))
test = "GAMA"
state, reward, done, time_pass, over = GAMA_connect(test) #connect
print("done:", done, "timepass:"******"restart acceleration: 0")
send_to_GAMA([[1, 0]])
#先传后计算
print(state)
rewards.append(reward) #contains the last
reward = torch.tensor([reward], dtype=torch.float, device=device)
rewards.append(reward) #contains the last
total_reward = sum(rewards).cpu().detach().numpy()
total_rewards.append(total_reward)
#state = torch.FloatTensor(state).reshape(1,4).to(device)
#last_value= critic(state)
with torch.autograd.set_detect_anomaly(True):
advantage = reward.detach(
) - value #+ last_value 最后一回的V(s+1) = 0
actor_loss = -(log_prob * advantage.detach())
critic_loss = (reward.detach() - value).pow(2) #+ last_value
lstm_loss = critic_loss
optimizerA.zero_grad()
optimizerC.zero_grad()
critic_loss.backward()
actor_loss.backward()
loss.append(critic_loss)
optimizerA.step()
optimizerC.step()
critic_next.load_state_dict(critic.state_dict())
print(
"----------------------------------Net_Trained---------------------------------------"
)
print('--------------------------Iteration:', episode,
'over--------------------------------')
episode += 1
log_probs = []
values = []
rewards = []
masks = []
loss_sum = sum(loss).cpu().detach().numpy()
total_loss.append(loss_sum)
cross_loss_curve(loss_sum.squeeze(0), total_reward, save_curve_pic,
save_critic_loss,
save_reward) #total_loss,total_rewards
loss = []
if episode > 50:
lr = 0.00008
new_lr = lr * (0.9**((episode - 40) // 10))
#if episode > 110:
# lr = 0.000008
# new_lr = lr * (0.9 ** ((episode-90) // 10)) #40
optimizerA = optim.Adam(actor.parameters(),
new_lr,
betas=(0.95, 0.999))
optimizerC = optim.Adam(critic.parameters(),
new_lr,
betas=(0.95, 0.999))
torch.save(actor.state_dict(), actor_path)
torch.save(critic.state_dict(), critic_path)
#最初の時
else:
print('Iteration:', episode)
state = np.reshape(state, (1, len(state))) #xxx
state_img = generate_img()
tensor_cv = torch.from_numpy(np.transpose(
state_img, (2, 0, 1))).double().to(device)
state = torch.DoubleTensor(state).reshape(1, 6).to(device)
value = critic(
state,
tensor_cv) #dist, # now is a tensoraction = dist.sample()
action, log_prob, entropy = actor(state, tensor_cv)
print("acceleration: ", action.cpu().numpy())
send_to_GAMA([[1, float(action.cpu().numpy() * 10)]])
log_prob = log_prob.unsqueeze(0)
entropy += entropy
state, reward, done, time_pass, over = GAMA_connect(test)
return None
