def __init__(self, mac, scheme, logger, args):
self.args = args
self.mac = mac
self.logger = logger
self.params = list(mac.parameters())
self.last_target_update_episode = 0
self.mixer = None
if args.mixer is not None:
if args.mixer == "vdn":
self.mixer = VDNMixer()
elif args.mixer == "qmix":
self.mixer = QMixer(args)
elif args.mixer == "graphmix":
self.mixer = GraphMixer(args)
else:
raise ValueError(f"Mixer {args.mixer} not recognized.")
self.params += list(self.mixer.parameters())
self.target_mixer = copy.deepcopy(self.mixer)
#self.optimiser = RMSprop(params=self.params, lr=args.lr, alpha=args.optim_alpha, eps=args.optim_eps)
self.optimizer = Adam(params=self.params, lr=args.lr)
self.target_mac = copy.deepcopy(mac)
self.log_stats_t = -self.args.learner_log_interval - 1
class QLearner:
def __init__(self, mac, scheme, logger, args):
self.args = args
self.mac = mac
self.logger = logger
self.params = list(mac.parameters())
self.last_target_update_episode = 0
self.mixer = None
if args.mixer is not None:
if args.mixer == "vdn":
self.mixer = VDNMixer()
elif args.mixer == "qmix":
self.mixer = QMixer(args)
elif args.mixer == "graphmix":
self.mixer = GraphMixer(args)
else:
raise ValueError(f"Mixer {args.mixer} not recognized.")
self.params += list(self.mixer.parameters())
self.target_mixer = copy.deepcopy(self.mixer)
#self.optimiser = RMSprop(params=self.params, lr=args.lr, alpha=args.optim_alpha, eps=args.optim_eps)
self.optimizer = Adam(params=self.params, lr=args.lr)
self.target_mac = copy.deepcopy(mac)
self.log_stats_t = -self.args.learner_log_interval - 1
#Main training part of q-learner
def train(self, batch: EpisodeBatch, t_env: int, episode_num: int):
# Get the relevant quantities
rewards = batch["reward"][:, :-1]
actions = batch["actions"][:, :-1]
terminated = batch["terminated"][:, :-1].float()
mask = batch["filled"][:, :-1].float()
mask[:, 1:] = mask[:, 1:] * (1 - terminated[:, :-1])
avail_actions = batch["avail_actions"]
# Calculate estimated Q-Values
mac_out = []
hidden_states = []
self.mac.init_hidden(batch.batch_size)
for t in range(batch.max_seq_length):
agent_outs = self.mac.forward(batch, t=t)
mac_out.append(agent_outs)
hidden_states.append(
self.mac.hidden_states.view(batch.batch_size,
self.args.n_agents, -1))
mac_out = th.stack(mac_out, dim=1) # Concat over time
hidden_states = th.stack(hidden_states, dim=1)
# Pick the Q-Values for the actions taken by each agent
chosen_action_qvals = th.gather(mac_out[:, :-1], dim=3,
index=actions).squeeze(
3) # Remove the last dim
# Calculate the Q-Values necessary for the target
target_mac_out = []
target_hidden_states = []
self.target_mac.init_hidden(batch.batch_size)
for t in range(batch.max_seq_length):
target_agent_outs = self.target_mac.forward(batch, t=t)
target_mac_out.append(target_agent_outs)
target_hidden_states.append(
self.target_mac.hidden_states.view(batch.batch_size,
self.args.n_agents, -1))
# We don't need the first timesteps Q-Value estimate for calculating targets
target_mac_out = th.stack(target_mac_out[1:],
dim=1) # Concat across time
target_hidden_states = th.stack(target_hidden_states[1:], dim=1)
# Mask out unavailable actions
target_mac_out[avail_actions[:, 1:] == 0] = -9999999
# Max over target Q-Values
if self.args.double_q:
# Get actions that maximise live Q (for double q-learning)
mac_out_detach = mac_out.clone().detach()
mac_out_detach[avail_actions == 0] = -9999999
cur_max_actions = mac_out_detach[:, 1:].max(dim=3, keepdim=True)[1]
target_max_qvals = th.gather(target_mac_out, 3,
cur_max_actions).squeeze(3)
else:
target_max_qvals = target_mac_out.max(dim=3)[0]
if self.args.mixer == 'graphmix':
# Mix
chosen_action_qvals_peragent = chosen_action_qvals.clone()
target_max_qvals_peragent = target_max_qvals.detach()
chosen_action_qvals, local_rewards, alive_agents_mask = self.mixer(
chosen_action_qvals,
batch["state"][:, :-1],
agent_obs=batch["obs"][:, :-1],
team_rewards=rewards,
hidden_states=hidden_states[:, :-1])
target_max_qvals = self.target_mixer(
target_max_qvals,
batch["state"][:, 1:],
agent_obs=batch["obs"][:, 1:],
hidden_states=target_hidden_states)[0]
## Global loss
# Calculate 1-step Q-Learning targets
targets = rewards + self.args.gamma * (
1 - terminated) * target_max_qvals
# Td-error
td_error = (chosen_action_qvals - targets.detach())
mask = mask.expand_as(td_error)
# 0-out the targets that came from padded data
masked_td_error = td_error * mask
# Normal L2 loss, take mean over actual data
global_loss = (masked_td_error**2).sum() / mask.sum()
## Local losses
# Calculate 1-step Q-Learning targets
local_targets = local_rewards + self.args.gamma * (1 - terminated).repeat(1, 1, self.args.n_agents) \
* target_max_qvals_peragent
# Td-error
local_td_error = (chosen_action_qvals_peragent - local_targets)
local_mask = mask.repeat(
1, 1, self.args.n_agents) * alive_agents_mask.float()
# 0-out the targets that came from padded data
local_masked_td_error = local_td_error * local_mask
# Normal L2 loss, take mean over actual data
local_loss = (local_masked_td_error**2).sum() / mask.sum()
# total loss
lambda_local = self.args.lambda_local
loss = global_loss + lambda_local * local_loss
else: # If the mixer is not graphmix
# Mix
if self.mixer is not None:
chosen_action_qvals = self.mixer(chosen_action_qvals,
batch["state"][:, :-1])
target_max_qvals = self.target_mixer(target_max_qvals,
batch["state"][:, 1:])
# Calculate 1-step Q-Learning targets
targets = rewards + self.args.gamma * (
1 - terminated) * target_max_qvals
# Td-error
td_error = (chosen_action_qvals - targets.detach())
mask = mask.expand_as(td_error)
# 0-out the targets that came from padded data
masked_td_error = td_error * mask
# Normal L2 loss, take mean over actual data
loss = (masked_td_error**2).sum() / mask.sum()
# Optimize
self.optimizer.zero_grad()
loss.backward()
grad_norm = th.nn.utils.clip_grad_norm_(self.params,
self.args.grad_norm_clip)
self.optimizer.step()
if (episode_num - self.last_target_update_episode
) / self.args.target_update_interval >= 1.0:
self._update_targets()
self.last_target_update_episode = episode_num
if t_env - self.log_stats_t >= self.args.learner_log_interval:
self.logger.log_stat("loss", loss.item(), t_env)
if self.args.mixer == 'graphmix':
self.logger.log_stat("global_loss", global_loss.item(), t_env)
self.logger.log_stat("local_loss", local_loss.item(), t_env)
self.logger.log_stat("grad_norm", grad_norm, t_env)
mask_elems = mask.sum().item()
self.logger.log_stat(
"td_error_abs",
(masked_td_error.abs().sum().item() / mask_elems), t_env)
self.logger.log_stat("q_taken_mean",
(chosen_action_qvals * mask).sum().item() /
(mask_elems * self.args.n_agents), t_env)
self.logger.log_stat("target_mean", (targets * mask).sum().item() /
(mask_elems * self.args.n_agents), t_env)
self.log_stats_t = t_env
def _update_targets(self):
self.target_mac.load_state(self.mac)
if self.mixer is not None:
self.target_mixer.load_state_dict(self.mixer.state_dict())
self.logger.console_logger.info("Updated target network")
def cuda(self):
self.mac.cuda()
self.target_mac.cuda()
if self.mixer is not None:
self.mixer.cuda()
self.target_mixer.cuda()
def save_models(self, path):
self.mac.save_models(path)
if self.mixer is not None:
th.save(self.mixer.state_dict(), f"{path}/mixer.th")
th.save(self.optimizer.state_dict(), f"{path}/opt.th")
def load_models(self, path):
self.mac.load_models(path)
self.target_mac.load_models(path)
if self.mixer is not None:
self.mixer.load_state_dict(
th.load(f"{path}/mixer.th",
map_location=lambda storage, loc: storage))
self.optimizer.load_state_dict(
th.load(f"{path}/opt.th",
map_location=lambda storage, loc: storage))
class QLearner:
def __init__(self, mac, scheme, logger, args):
self.args = args
self.mac = mac
self.logger = logger
self.params = list(mac.parameters())
self.last_target_update_episode = 0
self.mixer = None
if args.mixer is not None:
if args.mixer == "vdn":
self.mixer = VDNMixer()
elif args.mixer == "qmix":
self.mixer = QMixer(args)
elif args.mixer == "graphmix":
self.mixer = GraphMixer(args)
else:
raise ValueError("Mixer {} not recognised.".format(args.mixer))
self.params += list(self.mixer.parameters())
self.target_mixer = copy.deepcopy(self.mixer)
self.optimiser = RMSprop(params=self.params, lr=args.lr, alpha=args.optim_alpha, eps=args.optim_eps)
self.target_mac = copy.deepcopy(mac)
self.log_stats_t = -self.args.learner_log_interval - 1
def train(self, batch: EpisodeBatch, t_env: int, episode_num: int):
# 관련된 데이터를 가져온다.
rewards = batch["reward"][:, :-1]
actions = batch["actions"][:, :-1]
terminated = batch["terminated"][:, :-1].float()
mask = batch["filled"][:, :-1].float()
mask[:, 1:] = mask[:, 1:] * (1 - terminated[:, :-1])
avail_actions = batch["avail_actions"]
# Agent 개별의 Q값을 산출함
mac_out = []
hidden_states = []
self.mac.init_hidden(batch.batch_size)
for t in range(batch.max_seq_length):
agent_outs = self.mac.forward(batch, t=t)
mac_out.append(agent_outs)
hidden_states.append(self.mac.hidden_states.view(batch.batch_size, self.args.n_agents, -1))
mac_out = th.stack(mac_out, dim=1) # 시간순에 따라 Concat
hidden_states = th.stack(hidden_states, dim=1)
# Agent가 선택한 행동의 Q값을 뽑는다.
chosen_action_qvals = th.gather(mac_out[:, :-1], dim=3, index=actions).squeeze(3) # Remove the last dim
# Agent Target Network 의 개별 Q값을 산출함
target_mac_out = []
target_hidden_states = []
self.target_mac.init_hidden(batch.batch_size)
for t in range(batch.max_seq_length):
target_agent_outs = self.target_mac.forward(batch, t=t)
target_mac_out.append(target_agent_outs)
target_hidden_states.append(self.target_mac.hidden_states.view(batch.batch_size, self.args.n_agents, -1))
# target network는 next_state 기준 이므로 t=1부터 저장
target_mac_out = th.stack(target_mac_out[1:], dim=1) # Concat across time
target_hidden_states = th.stack(target_hidden_states[1:], dim=1)
# Mask out unavailable actions
target_mac_out[avail_actions[:, 1:] == 0] = -9999999
if self.args.double_q:
# Get actions that maximise live Q (for double q-learning)
# Agent의 Q값 저장
mac_out_detach = mac_out.clone().detach()
mac_out_detach[avail_actions == 0] = -9999999
# Agent의 next_state 기준의 최대 Q값의 Action 저장
cur_max_actions = mac_out_detach[:, 1:].max(dim=3, keepdim=True)[1]
# 이 action 에 대한 Target Network Q값을 저장한다.
# 실제 target_mac_out의 최댓값과 다를 수 있음
# 실제로 Agent가 선택한 행동의 대한 Q값을 가져오는 셈
target_max_qvals = th.gather(target_mac_out, 3, cur_max_actions).squeeze(3)
else:
target_max_qvals = target_mac_out.max(dim=3)[0]
if self.args.mixer == 'graphmix':
# Mix
chosen_action_qvals_peragent = chosen_action_qvals.clone()
target_max_qvals_peragent = target_max_qvals.detach()
Q_total, local_rewards, alive_agents_mask = self.mixer(chosen_action_qvals,
batch["state"][:, :-1],
agent_obs=batch["obs"][:, :-1],
team_rewards=rewards,
hidden_states=hidden_states[:, :-1]
)
target_Q_total = self.target_mixer(target_max_qvals,
batch["state"][:, 1:],
agent_obs=batch["obs"][:, 1:],
hidden_states=target_hidden_states
)[0]
## Global loss
# Calculate 1-step Q-Learning targets
targets = rewards + self.args.gamma * (1 - terminated) * target_Q_total
# Td-error
td_error = (Q_total - targets.detach())
mask = mask.expand_as(td_error)
# 0-out the targets that came from padded data
masked_td_error = td_error * mask
# Normal L2 loss, take mean over actual data
global_loss = (masked_td_error ** 2).sum() / mask.sum()
## Local losses
# Calculate 1-step Q-Learning targets
local_targets = local_rewards + self.args.gamma * (1 - terminated).repeat(1, 1, self.args.n_agents) \
* target_max_qvals_peragent
# Td-error
local_td_error = (chosen_action_qvals_peragent - local_targets)
local_mask = mask.repeat(1, 1, self.args.n_agents) * alive_agents_mask.float()
# 0-out the targets that came from padded data
local_masked_td_error = local_td_error * local_mask
# Normal L2 loss, take mean over actual data
local_loss = (local_masked_td_error ** 2).sum() / mask.sum()
# total loss
lambda_local = self.args.lambda_local
loss = global_loss + lambda_local * local_loss
else:
# Mix
if self.mixer is not None:
# Agent가 선택한 Q값과 state 정보를 넘겨 Q_total, target_Q_total 산출
Q_total = self.mixer(chosen_action_qvals, batch["state"][:, :-1])
target_Q_total = self.target_mixer(target_max_qvals, batch["state"][:, 1:])
else:
Q_total = chosen_action_qvals
target_Q_total = target_max_qvals
# 1 step Q 러닝 수행
targets = rewards + self.args.gamma * (1 - terminated) * target_Q_total
# Td-error
td_error = (Q_total - targets.detach())
mask = mask.expand_as(td_error)
# 0-out the targets that came from padded data
masked_td_error = td_error * mask
# Normal L2 loss, take mean over actual data
loss = (masked_td_error ** 2).sum() / mask.sum()
# Optimise
self.optimiser.zero_grad()
loss.backward()
grad_norm = th.nn.utils.clip_grad_norm_(self.params, self.args.grad_norm_clip)
self.optimiser.step()
if (episode_num - self.last_target_update_episode) / self.args.target_update_interval >= 1.0:
self._update_targets()
self.last_target_update_episode = episode_num
if t_env - self.log_stats_t >= self.args.learner_log_interval:
self.logger.log_stat("loss", loss.item(), t_env)
if self.args.mixer == 'graphmix':
self.logger.log_stat("global_loss", global_loss.item(), t_env)
self.logger.log_stat("local_loss", local_loss.item(), t_env)
self.logger.log_stat("grad_norm", grad_norm, t_env)
mask_elems = mask.sum().item()
self.logger.log_stat("td_error_abs", (masked_td_error.abs().sum().item() / mask_elems), t_env)
self.logger.log_stat("q_taken_mean",
(Q_total * mask).sum().item() / (mask_elems * self.args.n_agents), t_env)
self.logger.log_stat("target_mean", (targets * mask).sum().item() / (mask_elems * self.args.n_agents),
t_env)
self.log_stats_t = t_env
def _update_targets(self):
self.target_mac.load_state(self.mac)
if self.mixer is not None:
self.target_mixer.load_state_dict(self.mixer.state_dict())
self.logger.console_logger.info("Updated target network")
def cuda(self):
self.mac.cuda()
self.target_mac.cuda()
if self.mixer is not None:
self.mixer.cuda()
self.target_mixer.cuda()
def save_models(self, path):
self.mac.save_models(path)
if self.mixer is not None:
th.save(self.mixer.state_dict(), "{}/mixer.th".format(path))
th.save(self.optimiser.state_dict(), "{}/opt.th".format(path))
def load_models(self, path):
self.mac.load_models(path)
self.target_mac.load_models(path)
if self.mixer is not None:
self.mixer.load_state_dict(th.load("{}/mixer.th".format(path), map_location=lambda storage, loc: storage))
self.optimiser.load_state_dict(th.load("{}/opt.th".format(path), map_location=lambda storage, loc: storage))