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 == "qtran_base":
self.mixer = QTranBase(args)
elif args.mixer == "qtran_alt":
raise Exception("Not implemented here!")
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)
# a little wasteful to deepcopy (e.g. duplicates action selector), but should work for any MAC
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 == "qtran_base":
self.mixer = QTranBase(args)
elif args.mixer == "qtran_alt":
raise Exception("Not implemented here!")
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)
# a little wasteful to deepcopy (e.g. duplicates action selector), but should work for any MAC
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,
show_demo=False,
save_data=None):
# 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 = []
mac_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)
mac_hidden_states.append(self.mac.hidden_states)
mac_out = torch.stack(mac_out, dim=1) # Concat over time
mac_hidden_states = torch.stack(mac_hidden_states, dim=1)
mac_hidden_states = mac_hidden_states.reshape(batch.batch_size,
self.args.n_agents,
batch.max_seq_length,
-1).transpose(1,
2) #btav
# Pick the Q-Values for the actions taken by each agent
chosen_action_qvals = torch.gather(mac_out[:, :-1],
dim=3,
index=actions).squeeze(
3) # Remove the last dim
x_mac_out = mac_out.clone().detach()
x_mac_out[avail_actions == 0] = -9999999
max_action_qvals, max_action_index = x_mac_out[:, :-1].max(dim=3)
max_action_index = max_action_index.detach().unsqueeze(3)
is_max_action = (max_action_index == actions).int().float()
if show_demo:
q_i_data = chosen_action_qvals.detach().cpu().numpy()
q_data = (max_action_qvals -
chosen_action_qvals).detach().cpu().numpy()
# Calculate the Q-Values necessary for the target
target_mac_out = []
target_mac_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_mac_hidden_states.append(self.target_mac.hidden_states)
# We don't need the first timesteps Q-Value estimate for calculating targets
target_mac_out = torch.stack(target_mac_out[:],
dim=1) # Concat across time
target_mac_hidden_states = torch.stack(target_mac_hidden_states, dim=1)
target_mac_hidden_states = target_mac_hidden_states.reshape(
batch.batch_size, self.args.n_agents, batch.max_seq_length,
-1).transpose(1, 2) #btav
# Mask out unavailable actions
target_mac_out[avail_actions[:, :] == 0] = -9999999 # From OG deepmarl
mac_out_maxs = mac_out.clone()
mac_out_maxs[avail_actions == 0] = -9999999
# Best joint action computed by target agents
target_max_actions = target_mac_out.max(dim=3, keepdim=True)[1]
# Best joint-action computed by regular agents
max_actions_qvals, max_actions_current = mac_out_maxs[:, :].max(
dim=3, keepdim=True)
if self.args.mixer == "qtran_base":
# -- TD Loss --
# Joint-action Q-Value estimates
joint_qs, vs = self.mixer(batch[:, :-1], mac_hidden_states[:, :-1])
# Need to argmax across the target agents' actions to compute target joint-action Q-Values
if self.args.double_q:
max_actions_current_ = torch.zeros(
size=(batch.batch_size, batch.max_seq_length,
self.args.n_agents, self.args.n_actions),
device=batch.device)
max_actions_current_onehot = max_actions_current_.scatter(
3, max_actions_current[:, :], 1)
max_actions_onehot = max_actions_current_onehot
else:
max_actions = torch.zeros(
size=(batch.batch_size, batch.max_seq_length,
self.args.n_agents, self.args.n_actions),
device=batch.device)
max_actions_onehot = max_actions.scatter(
3, target_max_actions[:, :], 1)
target_joint_qs, target_vs = self.target_mixer(
batch[:, 1:],
hidden_states=target_mac_hidden_states[:, 1:],
actions=max_actions_onehot[:, 1:])
# Td loss targets
td_targets = rewards.reshape(-1, 1) + self.args.gamma * (
1 - terminated.reshape(-1, 1)) * target_joint_qs
td_error = (joint_qs - td_targets.detach())
masked_td_error = td_error * mask.reshape(-1, 1)
td_loss = (masked_td_error**2).sum() / mask.sum()
# -- TD Loss --
# -- Opt Loss --
# Argmax across the current agents' actions
if not self.args.double_q: # Already computed if we're doing double Q-Learning
max_actions_current_ = torch.zeros(
size=(batch.batch_size, batch.max_seq_length,
self.args.n_agents, self.args.n_actions),
device=batch.device)
max_actions_current_onehot = max_actions_current_.scatter(
3, max_actions_current[:, :], 1)
max_joint_qs, _ = self.mixer(
batch[:, :-1],
mac_hidden_states[:, :-1],
actions=max_actions_current_onehot[:, :-1]
) # Don't use the target network and target agent max actions as per author's email
# max_actions_qvals = torch.gather(mac_out[:, :-1], dim=3, index=max_actions_current[:,:-1])
opt_error = max_actions_qvals[:, :-1].sum(dim=2).reshape(
-1, 1) - max_joint_qs.detach() + vs
masked_opt_error = opt_error * mask.reshape(-1, 1)
opt_loss = (masked_opt_error**2).sum() / mask.sum()
# -- Opt Loss --
# -- Nopt Loss --
# target_joint_qs, _ = self.target_mixer(batch[:, :-1])
nopt_values = chosen_action_qvals.sum(dim=2).reshape(
-1, 1) - joint_qs.detach(
) + vs # Don't use target networks here either
nopt_error = nopt_values.clamp(max=0)
masked_nopt_error = nopt_error * mask.reshape(-1, 1)
nopt_loss = (masked_nopt_error**2).sum() / mask.sum()
# -- Nopt loss --
elif self.args.mixer == "qtran_alt":
raise Exception("Not supported yet.")
if show_demo:
tot_q_data = joint_qs.detach().cpu().numpy()
tot_target = td_targets.detach().cpu().numpy()
bs = q_data.shape[0]
tot_q_data = tot_q_data.reshape(bs, -1)
tot_target = tot_target.reshape(bs, -1)
print('action_pair_%d_%d' % (save_data[0], save_data[1]),
np.squeeze(q_data[:, 0]), np.squeeze(q_i_data[:, 0]),
np.squeeze(tot_q_data[:, 0]), np.squeeze(tot_target[:, 0]))
self.logger.log_stat(
'action_pair_%d_%d' % (save_data[0], save_data[1]),
np.squeeze(tot_q_data[:, 0]), t_env)
return
loss = td_loss + self.args.opt_loss * opt_loss + self.args.nopt_min_loss * nopt_loss
masked_hit_prob = torch.mean(is_max_action, dim=2) * mask
hit_prob = masked_hit_prob.sum() / mask.sum()
# Optimise
self.optimiser.zero_grad()
loss.backward()
grad_norm = torch.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)
self.logger.log_stat("hit_prob", hit_prob.item(), t_env)
self.logger.log_stat("td_loss", td_loss.item(), t_env)
self.logger.log_stat("opt_loss", opt_loss.item(), t_env)
self.logger.log_stat("nopt_loss", nopt_loss.item(), t_env)
self.logger.log_stat("grad_norm", grad_norm, t_env)
if self.args.mixer == "qtran_base":
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(
"td_targets",
((masked_td_error).sum().item() / mask_elems), t_env)
self.logger.log_stat("td_chosen_qs",
(joint_qs.sum().item() / mask_elems),
t_env)
self.logger.log_stat("v_mean", (vs.sum().item() / mask_elems),
t_env)
self.logger.log_stat(
"agent_indiv_qs",
((chosen_action_qvals * 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:
torch.save(self.mixer.state_dict(), "{}/mixer.torch".format(path))
torch.save(self.optimiser.state_dict(), "{}/opt.torch".format(path))
def load_models(self, path):
self.mac.load_models(path)
# Not quite right but I don't want to save target networks
self.target_mac.load_models(path)
if self.mixer is not None:
self.mixer.load_state_dict(
torch.load("{}/mixer.torch".format(path),
map_location=lambda storage, loc: storage))
self.optimiser.load_state_dict(
torch.load("{}/opt.torch".format(path),
map_location=lambda storage, loc: storage))