def __init__(self, mac, scheme, logger, args):
self.args = args
self.mac = mac
self.logger = logger
self.params = list(mac.parameters())
#added by keegan
self.critic = LIIRCritic(scheme, args).to(device)
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 = NoiseQMixer(args)
else:
raise ValueError("Mixer {} not recognised.".format(args.mixer))
self.params += list(self.mixer.parameters())
self.target_mixer = copy.deepcopy(self.mixer)
discrim_input = np.prod(self.args.state_shape) + self.args.n_agents * self.args.n_actions
self.n_agents = self.args.n_agents
if self.args.rnn_discrim:
self.rnn_agg = RNNAggregator(discrim_input, args)
self.discrim = Discrim(args.rnn_agg_size, self.args.noise_dim, args)
self.params += list(self.discrim.parameters())
self.params += list(self.rnn_agg.parameters())
else:
self.discrim = Discrim(discrim_input, self.args.noise_dim, args)
self.params += list(self.discrim.parameters())
self.discrim_loss = th.nn.CrossEntropyLoss(reduction="none")
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
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 = NoiseQMixer(args)
else:
raise ValueError("Mixer {} not recognised.".format(args.mixer))
self.params += list(self.mixer.parameters())
self.target_mixer = copy.deepcopy(self.mixer)
discrim_input = np.prod(
self.args.state_shape) + self.args.n_agents * self.args.n_actions
if self.args.rnn_discrim:
self.rnn_agg = RNNAggregator(discrim_input, args)
self.discrim = Discrim(args.rnn_agg_size, self.args.noise_dim,
args)
self.params += list(self.discrim.parameters())
self.params += list(self.rnn_agg.parameters())
else:
self.discrim = Discrim(discrim_input, self.args.noise_dim, args)
self.params += list(self.discrim.parameters())
self.discrim_loss = th.nn.CrossEntropyLoss(reduction="none")
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):
# 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"]
noise = batch["noise"][:,
0].unsqueeze(1).repeat(1, rewards.shape[1], 1)
# Calculate estimated Q-Values
mac_out = []
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_out = th.stack(mac_out, dim=1) # Concat over time
# 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 = []
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)
# 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
# Mask out unavailable actions
#target_mac_out[avail_actions[:, 1:] == 0] = -9999999 # From OG deepmarl
# Max over target Q-Values
if self.args.double_q:
# Get actions that maximise live Q (for double q-learning)
#mac_out[avail_actions == 0] = -9999999
cur_max_actions = mac_out[:, 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]
# Mix
if self.mixer is not None:
chosen_action_qvals = self.mixer(chosen_action_qvals,
batch["state"][:, :-1], noise)
target_max_qvals = self.target_mixer(target_max_qvals,
batch["state"][:, 1:], noise)
# Discriminator
#mac_out[avail_actions == 0] = -9999999
q_softmax_actions = th.nn.functional.softmax(mac_out[:, :-1], dim=3)
if self.args.hard_qs:
maxs = th.max(mac_out[:, :-1], dim=3, keepdim=True)[1]
zeros = th.zeros_like(q_softmax_actions)
zeros.scatter_(dim=3, index=maxs, value=1)
q_softmax_actions = zeros
q_softmax_agents = q_softmax_actions.reshape(
q_softmax_actions.shape[0], q_softmax_actions.shape[1], -1)
states = batch["state"][:, :-1]
state_and_softactions = th.cat([q_softmax_agents, states], dim=2)
if self.args.rnn_discrim:
h_to_use = th.zeros(size=(batch.batch_size,
self.args.rnn_agg_size)).to(
states.device)
hs = th.ones_like(h_to_use)
for t in range(batch.max_seq_length - 1):
hs = self.rnn_agg(state_and_softactions[:, t], hs)
for b in range(batch.batch_size):
if t == batch.max_seq_length - 2 or (mask[b, t] == 1 and
mask[b, t + 1] == 0):
# This is the last timestep of the sequence
h_to_use[b] = hs[b]
s_and_softa_reshaped = h_to_use
else:
s_and_softa_reshaped = state_and_softactions.reshape(
-1, state_and_softactions.shape[-1])
if self.args.mi_intrinsic:
s_and_softa_reshaped = s_and_softa_reshaped.detach()
discrim_prediction = self.discrim(s_and_softa_reshaped)
# Cross-Entropy
target_repeats = 1
if not self.args.rnn_discrim:
target_repeats = q_softmax_actions.shape[1]
discrim_target = batch["noise"][:, 0].long().detach().max(
dim=1)[1].unsqueeze(1).repeat(1, target_repeats).reshape(-1)
discrim_loss = self.discrim_loss(discrim_prediction, discrim_target)
if self.args.rnn_discrim:
averaged_discrim_loss = discrim_loss.mean()
else:
masked_discrim_loss = discrim_loss * mask.reshape(-1)
averaged_discrim_loss = masked_discrim_loss.sum() / mask.sum()
self.logger.log_stat("discrim_loss", averaged_discrim_loss.item(),
t_env)
# Calculate 1-step Q-Learning targets
targets = rewards + self.args.gamma * (1 -
terminated) * target_max_qvals
if self.args.mi_intrinsic:
assert self.args.rnn_discrim is False
targets = targets + self.args.mi_scaler * discrim_loss.view_as(
rewards)
# 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()
loss = loss + self.args.mi_loss * averaged_discrim_loss
# 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)
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()
self.discrim.cuda()
if self.args.rnn_discrim:
self.rnn_agg.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))