def train_v2(self, model_path, log_path):
'''
every time slot, it updates netowrk parameters like TD-error
'''
self.epi_rewards_mean, self.epi_durations, self.episode = [], [], []
self.actor_losses, self.critic_losses = [], []
self.step_count = []
for epi in range(self.conf['num_episode']): # episodes
log_probs, values, rewards, masks = [], [], [], []
entropy, epi_reward, epi_duration, step = 0, 0.0, 0.0, 0
print("--- episode %s ---" % (epi))
self.episode.append(epi)
state = self.env.reset() # [2*num_plant, 1]
t = P.t_start
while t < P.t_end: # one episode (simulation)
epi_duration = t
if round(t,
3) * 1000 % 10 == 0: # every 10 ms, schedule udpate
# action = self.agent.select_action(state_ts) # action type: tensor [1X1]
state_ts = to_tensor(state).reshape(-1) # [1]
dist, value = self.agent.actor(
state_ts), self.agent.critic(
state_ts) # pi(s) and V(s)
action = dist.sample() # scalar of a tensor
next_state, reward, done, info = self.env.step(
action.cpu().numpy().item(),
t) # shape of next_state : [(2*num_plant) X 1]
actor_loss, critic_loss = self.agent.optimization_model_v2(
dist, action, state_ts,
to_tensor(next_state).reshape(-1), reward, done)
state = next_state
epi_reward += reward
step += 1
self.step_count.append(step)
rewards.append(reward)
self.actor_losses.append(actor_loss)
self.critic_losses.append(critic_loss)
if done:
break
else: # every 1 ms
self.env.update_plant_state(t) # plant status update
t = t + P.Ts
# optimize
print("epi_duration:", epi_duration)
print("mean epi_reward:", epi_reward / len(rewards))
# episode done
self.epi_rewards_mean.append(epi_reward / len(rewards))
self.epi_durations.append(epi_duration)
# Save satet_dict
torch.save(self.agent.actor.state_dict(), model_path)
# torch.save(self.agent.critic.state_dict(), CRITIC_MODEL)
self.save_log(log_path)
def train(self, model_path, log_path):
self.epi_durations, self.epi_rewards_mean, self.episode, self.step_count, self.epi_loss = [], [], [], [], []
for epi in range(self.conf['num_episode']): # episodes
rewards = []
self.episode.append(epi)
step = 0
epi_reward = 0.0
print("--- episode %s ---" % (epi))
state = self.env.reset()
state_ts = to_tensor(state).reshape(-1).unsqueeze(0)
t = P.t_start
while t < P.t_end: # one episode (simulation)
epi_duration = t
if round(t,
3) * 1000 % 10 == 0: # every 10 ms, schedule udpate
action = self.agent.select_action(state_ts)
next_state, reward, done, info = self.env.step(
action.item(),
t) # shape of next_state : [(2*num_plant) X 1]
done_mask = 0.0 if done else 1.0
next_state_ts = to_tensor(next_state).reshape(
-1).unsqueeze(0)
reward_ts = to_tensor(np.asarray(reward).reshape(-1))
self.agent.memory.push_transition(state_ts, action,
next_state_ts, reward_ts)
state_ts = next_state_ts
epi_reward += reward
step += 1
self.step_count.append(step)
rewards.append(reward)
if self.agent.memory.length(
) >= self.conf['memory_capacity']:
loss = self.agent.update()
self.epi_loss.append(loss)
if done:
break
else: # every 1 ms
self.env.update_plant_state(t) # plant status update
t = t + P.Ts
if epi % self.conf['target_update'] == 0 and epi != 0:
print("target update")
self.agent.q_target.load_state_dict(self.agent.q.state_dict())
self.epi_durations.append(epi_duration)
self.epi_rewards_mean.append(epi_reward)
print("epi_duration:", epi_duration)
# print("epi_reward:%s, len:%s"%(epi_reward, len(rewards)))
print("mean epi_reward:", epi_reward / len(rewards))
if epi % 10 == 0 and epi != 0:
torch.save(self.agent.q.state_dict(), model_path)
self.save_log(log_path)
torch.save(self.agent.q.state_dict(), model_path)
self.save_log(log_path)
def test(self, env, model_path):
test_actions = []
# new agent
new_agent = DQN(self.conf, self.device)
new_agent.load_model(model_path)
epi_reward = 0.0
epi_duration = 0.0
state = env.reset() # [2*num_plant, 1]
state_ts = to_tensor(state).unsqueeze(
0
) # [1, 2*num_plant, 1] # unsqueeze(0) on 'state' is necessary for reply memory
# realtimePlot = testDataPlotter(self.conf)
t = P.t_start
while t < P.t_end:
t_next_plot = t + P.t_plot
epi_duration = t
while t < t_next_plot: # data plot period
if round(t,
3) * 1000 % 10 == 0: # every 10 ms, schedule udpate
action = new_agent.select_action(
state_ts) # action type: tensor [1X1]
next_state, reward, done, info = env.step(
action.item(),
t) # shape of next_state : [(2*num_plant) X 1]
epi_reward += reward
test_actions.append(action.item())
if done: break
else: # every 1 ms
env.update_plant_state(t) # plant status update
t = t + P.Ts
# self.update_dataPlot(realtimePlot, t, env) # update data plot
if done: break
def optimization_model(self, next_state, rewards, log_probs, values, masks):
'''
next_state : episode last state, which is used for G_t (return)
rewards : a list that includes all rewards during an episode
log_probs : a list that includes all log pi(a_t|s_t) during an episode, relative to actor network
values : a list that includes all V(s_t), relative to critic network
'''
next_state_ts = to_tensor(next_state).reshape(-1) # [5*num_plant]
next_value = self.critic(next_state_ts) # V(s_{t+1}) 전체 분포?
returns = self.compute_returns(next_value, rewards, masks) # G_t = R + gamma * G_{t+1}
log_probs = torch.cat(log_probs)
returns = torch.cat(returns).detach()
values = torch.cat(values)
advantage = returns - values # A = G_t - V(s_t)
actor_loss = -(log_probs * advantage.detach()).mean() # -(log_pi(a|s) * A) 의 평균
critic_loss = advantage.pow(2).mean() # A^2 의 평균?
self.optimizerA.zero_grad()
self.optimizerC.zero_grad()
actor_loss.backward()
critic_loss.backward()
self.optimizerA.step()
self.optimizerC.step()
return actor_loss, critic_loss
def scheduler(self, state):
'''
input: states for all plants
output: current scheduled plant
'''
state_ts = to_tensor(state, is_cuda=False,
device=self.device).reshape(-1)
# print("state_ts:", state_ts)
dist = self.agent.actor(state_ts)
action = dist.sample()
return action
def select_action(self, state, noise_enable=True, decay_epsilon=True):
action, _ = self.actor(
to_tensor(state).reshape(-1).unsqueeze(0)
) # input shape = [batch(=1) X state_dim], action : type (tuple), shape [batch X action_dim]
action = action.cpu().detach().numpy().squeeze(
0) # action shape [action_dim,]
if noise_enable == True:
action += self.is_training * max(self.epsilon,
0) * self.random_process.sample()
action = np.clip(action, 0.,
1.) # input 중 -1~1 을 벗어나는 값에 대해 -1 or 1 로 대체
if decay_epsilon:
self.epsilon -= self.depsilon
return action
def action_and_envStep(self, agent, env, t, state, algorithm):
if algorithm == 'A2C' or algorithm == 'random':
state_ts = to_tensor(state).reshape(-1)
dist = agent.actor(state_ts)
action = dist.sample()
# schedule = env.action_to_schedule_v2(action.cpu().numpy(), self.conf['action_dim'])
next_state, reward, done, info = env.step(
action.cpu().numpy().item(), t)
elif algorithm == 'sequence':
action = agent.select_seqAction()
# schedule = env.action_to_schedule_v2(action.cpu().numpy(), self.conf['action_dim'])
next_state, reward, done, info = env.step(
action.cpu().numpy().item(), t)
if done == True: print("done true")
return action, next_state, reward, done, info
def update_policy(self, memory, gamma=0.99):
print("updating...")
# Sample batch
experiences = memory.sample(
self.conf['batch_size']
) # type: list | shape: (max_epi_length(2000)-1 X batch(32) X 5(??))
if len(experiences) == 0: # not enough samples
return
dtype = torch.cuda.FloatTensor
policy_loss_total = 0
value_loss_total = 0
for t in range(len(experiences) - 1): # iterate over episodes
# print("t:", t)
target_cx = Variable(torch.zeros(self.conf['batch_size'],
50)).type(dtype)
target_hx = Variable(torch.zeros(self.conf['batch_size'],
50)).type(dtype)
cx = Variable(torch.zeros(self.conf['batch_size'], 50)).type(dtype)
hx = Variable(torch.zeros(self.conf['batch_size'], 50)).type(dtype)
# we first get the data out of the sampled experience
# shape of state0, action, reward: [batch X state_dim], [batch X 1], [batch X 1]
state0 = np.stack([
trajectory.state0 for trajectory in experiences[t]
]) # batch 개수만큼 각 epi 중 t 시점에서 상태만 추출
# action = np.expand_dims(np.stack((trajectory.action for trajectory in experiences[t])), axis=1)
action = np.stack(
[trajectory.action for trajectory in experiences[t]])
reward = np.expand_dims(np.stack(
[trajectory.reward for trajectory in experiences[t]]),
axis=1)
# reward = np.stack((trajectory.reward for trajectory in experiences[t]))
state1 = np.stack(
[trajectory.state0 for trajectory in experiences[t + 1]])
target_action, (target_hx, target_cx) = self.actor_target(
to_tensor(state1).reshape(self.conf['batch_size'], -1),
(target_hx, target_cx))
next_q_value = self.critic_target([
to_tensor(state1).reshape(self.conf['batch_size'], -1),
target_action
])
target_q = to_tensor(reward) + gamma * next_q_value
# Critic update
current_q = self.critic([
to_tensor(state0).reshape(self.conf['batch_size'], -1),
to_tensor(action)
])
value_loss = F.smooth_l1_loss(current_q, target_q)
value_loss /= len(experiences) # divide by trajectory length
value_loss_total += value_loss
# update per trajectory
self.critic.zero_grad()
value_loss.backward()
# Actor update
action, (hx, cx) = self.actor(
to_tensor(state0).reshape(self.conf['batch_size'], -1),
(hx, cx))
policy_loss = -self.critic([
to_tensor(state0).reshape(self.conf['batch_size'], -1), action
])
policy_loss /= len(experiences) # divide by trajectory length
policy_loss_total += policy_loss.mean()
policy_loss = policy_loss.mean()
self.actor.zero_grad()
policy_loss.backward()
self.critic_optim.step()
self.actor_optim.step()
# Target update
soft_update(self.actor_target, self.actor, self.tau)
soft_update(self.critic_target, self.critic, self.tau)
print("update finish!")
def train(self, model_path, log_path):
self.epi_rewards_mean, self.epi_durations, self.episode = [], [], []
self.actor_losses, self.critic_losses = [], []
self.step_count = []
for epi in range(self.conf['num_episode']): # episodes
log_probs, values, rewards, masks = [], [], [], []
entropy, epi_reward, epi_duration, step = 0, 0.0, 0.0, 0
print("--- episode %s ---" % (epi))
self.episode.append(epi)
state = self.env.reset() # [2*num_plant, 1]
t = P.t_start
while t < P.t_end: # one episode (simulation)
epi_duration = t
if round(t,
3) * 1000 % 10 == 0: # every 10 ms, schedule udpate
# action = self.agent.select_action(state_ts) # action type: tensor [1X1]
state_ts = to_tensor(state).reshape(-1) # [1]
dist, value = self.agent.actor(
state_ts), self.agent.critic(
state_ts) # pi(s) and V(s)
action = dist.sample() # scalar of a tensor
next_state, reward, done, info = self.env.step(
action.cpu().numpy().item(),
t) # shape of next_state : [(2*num_plant) X 1]
log_prob = dist.log_prob(action).unsqueeze(
0) # [1] : log pi(a_t|s_t)
entropy += dist.entropy().mean()
log_probs.append(log_prob)
values.append(value)
rewards.append(
torch.tensor([reward],
dtype=torch.float,
device=self.device))
masks.append(
torch.tensor([1 - done],
dtype=torch.float,
device=self.device))
# print("step reward: ", reward)
state = next_state
epi_reward += reward
step += 1
self.step_count.append(step)
if done:
break
else: # every 1 ms
self.env.update_plant_state(t) # plant status update
t = t + P.Ts
# optimize - monte-carlo
actor_loss, critic_loss = self.agent.optimization_model(
next_state, rewards, log_probs, values, masks)
print("epi_duration:", epi_duration)
print("mean epi_reward:", epi_reward / len(rewards))
# episode done
self.epi_rewards_mean.append(epi_reward / len(rewards))
self.epi_durations.append(epi_duration)
self.actor_losses.append(actor_loss.item())
self.critic_losses.append(critic_loss.item())
if epi % 10 == 0:
torch.save(self.agent.actor.state_dict(), model_path)
self.save_log(log_path)
# Save satet_dict
torch.save(self.agent.actor.state_dict(), model_path)
self.save_log(log_path)
