def __init__(self, mac, scheme, logger, args):
self.args = args
self.mac = mac
self.logger = logger
self.mac_params = list(mac.parameters())
self.params = list(self.mac.parameters())
self.last_target_update_episode = 0
self.mixer = None
assert args.mixer is not None
if args.mixer is not None:
if args.mixer == "vdn":
self.mixer = VDNMixer()
elif args.mixer == "qmix":
self.mixer = QMixer(args)
else:
raise ValueError("Mixer {} not recognised.".format(args.mixer))
self.mixer_params = list(self.mixer.parameters())
self.params += list(self.mixer.parameters())
self.target_mixer = copy.deepcopy(self.mixer)
# a little wasteful to deepcopy (e.g. duplicates action selector), but should work for any MAC
self.target_mac = copy.deepcopy(mac)
# Central Q
# TODO: Clean this mess up!
self.central_mac = None
assert self.args.central_mixer == "ff"
self.central_mixer = QMixerCentralFF(args)
assert args.central_mac == "basic_central_mac"
self.central_mac = mac_REGISTRY[args.central_mac](
scheme, args
) # Groups aren't used in the CentralBasicController. Little hacky
self.target_central_mac = copy.deepcopy(self.central_mac)
self.params += list(self.central_mac.parameters())
self.params += list(self.central_mixer.parameters())
self.target_central_mixer = copy.deepcopy(self.central_mixer)
self.optimiser = RMSprop(params=self.params,
lr=args.lr,
alpha=args.optim_alpha,
eps=args.optim_eps)
self.log_stats_t = -self.args.learner_log_interval - 1
def __init__(self, mac, scheme, logger, args):
self.args = args
self.mac = mac
self.logger = logger
self.mac_params = list(mac.parameters())
self.params = list(self.mac.parameters())
self.last_target_update_episode = 0
self.mixer = None
assert args.mixer is not None
if args.mixer is not None:
if args.mixer == "vdn":
self.mixer = VDNMixer()
elif args.mixer == "qmix":
self.mixer = QMixer(args)
else:
raise ValueError("Mixer {} not recognised.".format(args.mixer))
self.mixer_params = list(self.mixer.parameters())
self.params += list(self.mixer.parameters())
self.target_mixer = copy.deepcopy(self.mixer)
# a little wasteful to deepcopy (e.g. duplicates action selector), but should work for any MAC
self.target_mac = copy.deepcopy(mac)
# Central Q
# TODO: Clean this mess up!
self.central_mac = None
if self.args.central_mixer in ["ff", "atten"]:
if self.args.central_loss == 0:
self.central_mixer = self.mixer
self.central_mac = self.mac
self.target_central_mac = self.target_mac
else:
if self.args.central_mixer == "ff":
self.central_mixer = QMixerCentralFF(
args
) # Feedforward network that takes state and agent utils as input
# elif self.args.central_mixer == "atten":
# self.central_mixer = QMixerCentralAtten(args)
else:
raise Exception("Error with central_mixer")
assert args.central_mac == "basic_central_mac"
self.central_mac = mac_REGISTRY[args.central_mac](
scheme, args
) # Groups aren't used in the CentralBasicController. Little hacky
self.target_central_mac = copy.deepcopy(self.central_mac)
self.params += list(self.central_mac.parameters())
else:
raise Exception("Error with qCentral")
self.params += list(self.central_mixer.parameters())
self.target_central_mixer = copy.deepcopy(self.central_mixer)
print('Mixer Size: ')
print(
get_parameters_num(
list(self.mixer.parameters()) +
list(self.central_mixer.parameters())))
self.optimiser = Adam(params=self.params, lr=args.lr)
self.log_stats_t = -self.args.learner_log_interval - 1
self.grad_norm = 1
self.mixer_norm = 1
self.mixer_norms = deque([1], maxlen=100)
class MAXQLearner:
def __init__(self, mac, scheme, logger, args):
self.args = args
self.mac = mac
self.logger = logger
self.mac_params = list(mac.parameters())
self.params = list(self.mac.parameters())
self.last_target_update_episode = 0
self.mixer = None
assert args.mixer is not None
if args.mixer is not None:
if args.mixer == "vdn":
self.mixer = VDNMixer()
elif args.mixer == "qmix":
self.mixer = QMixer(args)
else:
raise ValueError("Mixer {} not recognised.".format(args.mixer))
self.mixer_params = list(self.mixer.parameters())
self.params += list(self.mixer.parameters())
self.target_mixer = copy.deepcopy(self.mixer)
# a little wasteful to deepcopy (e.g. duplicates action selector), but should work for any MAC
self.target_mac = copy.deepcopy(mac)
# Central Q
# TODO: Clean this mess up!
self.central_mac = None
if self.args.central_mixer in ["ff", "atten"]:
if self.args.central_loss == 0:
self.central_mixer = self.mixer
self.central_mac = self.mac
self.target_central_mac = self.target_mac
else:
if self.args.central_mixer == "ff":
self.central_mixer = QMixerCentralFF(
args
) # Feedforward network that takes state and agent utils as input
# elif self.args.central_mixer == "atten":
# self.central_mixer = QMixerCentralAtten(args)
else:
raise Exception("Error with central_mixer")
assert args.central_mac == "basic_central_mac"
self.central_mac = mac_REGISTRY[args.central_mac](
scheme, args
) # Groups aren't used in the CentralBasicController. Little hacky
self.target_central_mac = copy.deepcopy(self.central_mac)
self.params += list(self.central_mac.parameters())
else:
raise Exception("Error with qCentral")
self.params += list(self.central_mixer.parameters())
self.target_central_mixer = copy.deepcopy(self.central_mixer)
print('Mixer Size: ')
print(
get_parameters_num(
list(self.mixer.parameters()) +
list(self.central_mixer.parameters())))
self.optimiser = Adam(params=self.params, lr=args.lr)
self.log_stats_t = -self.args.learner_log_interval - 1
self.grad_norm = 1
self.mixer_norm = 1
self.mixer_norms = deque([1], maxlen=100)
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 = []
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_agents = th.gather(mac_out[:, :-1],
dim=3,
index=actions).squeeze(
3) # Remove the last dim
chosen_action_qvals = chosen_action_qvals_agents
# 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[:],
dim=1) # Concat across time
# Mask out unavailable actions
target_mac_out[avail_actions[:, :] == 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_detach = mac_out.clone().detach()
mac_out_detach[avail_actions == 0] = -9999999
cur_max_action_targets, cur_max_actions = mac_out_detach[:, :].max(
dim=3, keepdim=True)
target_max_agent_qvals = th.gather(
target_mac_out[:, :], 3, cur_max_actions[:, :]).squeeze(3)
else:
raise Exception("Use double q")
# Central MAC stuff
central_mac_out = []
self.central_mac.init_hidden(batch.batch_size)
for t in range(batch.max_seq_length):
agent_outs = self.central_mac.forward(batch, t=t)
central_mac_out.append(agent_outs)
central_mac_out = th.stack(central_mac_out, dim=1) # Concat over time
central_chosen_action_qvals_agents = th.gather(
central_mac_out[:, :-1],
dim=3,
index=actions.unsqueeze(4).repeat(
1, 1, 1, 1, self.args.central_action_embed)).squeeze(
3) # Remove the last dim
central_target_mac_out = []
self.target_central_mac.init_hidden(batch.batch_size)
for t in range(batch.max_seq_length):
target_agent_outs = self.target_central_mac.forward(batch, t=t)
central_target_mac_out.append(target_agent_outs)
central_target_mac_out = th.stack(central_target_mac_out[:],
dim=1) # Concat across time
# Mask out unavailable actions
central_target_mac_out[avail_actions[:, :] ==
0] = -9999999 # From OG deepmarl
# Use the Qmix max actions
central_target_max_agent_qvals = th.gather(
central_target_mac_out[:, :], 3,
cur_max_actions[:, :].unsqueeze(4).repeat(
1, 1, 1, 1, self.args.central_action_embed)).squeeze(3)
# ---
# Mix
chosen_action_qvals = self.mixer(chosen_action_qvals,
batch["state"][:, :-1])
target_max_qvals = self.target_central_mixer(
central_target_max_agent_qvals, batch["state"])
# We use the calculation function of sarsa lambda to approximate q star lambda
targets = build_td_lambda_targets(rewards, terminated, mask,
target_max_qvals, self.args.n_agents,
self.args.gamma, self.args.td_lambda)
# 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
# Training central Q
central_chosen_action_qvals = self.central_mixer(
central_chosen_action_qvals_agents, batch["state"][:, :-1])
central_td_error = (central_chosen_action_qvals - targets.detach())
central_mask = mask.expand_as(central_td_error)
central_masked_td_error = central_td_error * central_mask
central_loss = 0.5 * (central_masked_td_error**2).sum() / mask.sum()
# QMIX loss with weighting
ws = th.ones_like(td_error) * self.args.w
if self.args.hysteretic_qmix: # OW-QMIX
ws = th.where(td_error < 0,
th.ones_like(td_error) * 1,
ws) # Target is greater than current max
w_to_use = ws.mean().item() # For logging
else: # CW-QMIX
is_max_action = (actions == cur_max_actions[:, :-1]).min(dim=2)[0]
max_action_qtot = self.target_central_mixer(
central_target_max_agent_qvals[:, :-1], batch["state"][:, :-1])
qtot_larger = targets > max_action_qtot
ws = th.where(is_max_action | qtot_larger,
th.ones_like(td_error) * 1,
ws) # Target is greater than current max
w_to_use = ws.mean().item() # Average of ws for logging
qmix_loss = (ws.detach() * (masked_td_error**2)).sum() / mask.sum()
# The weightings for the different losses aren't used (they are always set to 1)
loss = self.args.qmix_loss * qmix_loss + self.args.central_loss * central_loss
# Optimise
self.optimiser.zero_grad()
loss.backward()
# Logging
agent_norm = 0
for p in self.mac_params:
param_norm = p.grad.data.norm(2)
agent_norm += param_norm.item()**2
agent_norm = agent_norm**(1. / 2)
mixer_norm = 0
for p in self.mixer_params:
param_norm = p.grad.data.norm(2)
mixer_norm += param_norm.item()**2
mixer_norm = mixer_norm**(1. / 2)
self.mixer_norm = mixer_norm
self.mixer_norms.append(mixer_norm)
grad_norm = th.nn.utils.clip_grad_norm_(self.params,
self.args.grad_norm_clip)
self.grad_norm = grad_norm
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("qmix_loss", qmix_loss.item(), t_env)
self.logger.log_stat("grad_norm", grad_norm, t_env)
self.logger.log_stat("mixer_norm", mixer_norm, t_env)
self.logger.log_stat("agent_norm", agent_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.logger.log_stat("central_loss", central_loss.item(), t_env)
self.logger.log_stat("w_to_use", w_to_use, 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())
if self.central_mac is not None:
self.target_central_mac.load_state(self.central_mac)
self.target_central_mixer.load_state_dict(
self.central_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()
if self.central_mac is not None:
self.central_mac.cuda()
self.target_central_mac.cuda()
self.central_mixer.cuda()
self.target_central_mixer.cuda()
# TODO: Model saving/loading is out of date!
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)
# 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(
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))
class SACQLearner:
def __init__(self, mac, scheme, logger, args):
self.args = args
self.mac = mac
self.logger = logger
self.mac_params = list(mac.parameters())
self.params = list(self.mac.parameters())
self.last_target_update_episode = 0
self.mixer = None
assert args.mixer is not None
if args.mixer is not None:
if args.mixer == "vdn":
self.mixer = VDNMixer()
elif args.mixer == "qmix":
self.mixer = QMixer(args)
else:
raise ValueError("Mixer {} not recognised.".format(args.mixer))
self.mixer_params = list(self.mixer.parameters())
self.params += list(self.mixer.parameters())
self.target_mixer = copy.deepcopy(self.mixer)
# a little wasteful to deepcopy (e.g. duplicates action selector), but should work for any MAC
self.target_mac = copy.deepcopy(mac)
# Central Q
# TODO: Clean this mess up!
self.central_mac = None
assert self.args.central_mixer == "ff"
self.central_mixer = QMixerCentralFF(args)
assert args.central_mac == "basic_central_mac"
self.central_mac = mac_REGISTRY[args.central_mac](
scheme, args
) # Groups aren't used in the CentralBasicController. Little hacky
self.target_central_mac = copy.deepcopy(self.central_mac)
self.params += list(self.central_mac.parameters())
self.params += list(self.central_mixer.parameters())
self.target_central_mixer = copy.deepcopy(self.central_mixer)
self.optimiser = RMSprop(params=self.params,
lr=args.lr,
alpha=args.optim_alpha,
eps=args.optim_eps)
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"]
# Current policies
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
# Mask out unavailable actions, renormalise (as in action selection)
mac_out[avail_actions == 0] = 0
mac_out = mac_out / mac_out.sum(dim=-1, keepdim=True)
mac_out[avail_actions == 0] = 0
mac_out[(mac_out.sum(dim=-1, keepdim=True) == 0).expand_as(
mac_out
)] = 1 # Set any all 0 probability vectors to all 1s. They will be masked out later, but still need to be sampled.
# Target policies
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)
target_mac_out = th.stack(target_mac_out, dim=1) # Concat across time
# Mask out unavailable actions, renormalise (as in action selection)
target_mac_out[avail_actions == 0] = 0
target_mac_out = target_mac_out / target_mac_out.sum(dim=-1,
keepdim=True)
target_mac_out[avail_actions == 0] = 0
target_mac_out[(
target_mac_out.sum(dim=-1, keepdim=True) == 0
).expand_as(
target_mac_out
)] = 1 # Set any all 0 probability vectors to all 1s. They will be masked out later, but still need to be sampled.
# Sample actions
sampled_actions = Categorical(mac_out).sample().long()
sampled_target_actions = Categorical(target_mac_out).sample().long()
# Central MAC stuff
central_mac_out = []
self.central_mac.init_hidden(batch.batch_size)
for t in range(batch.max_seq_length):
agent_outs = self.central_mac.forward(batch, t=t)
central_mac_out.append(agent_outs)
central_mac_out = th.stack(central_mac_out, dim=1) # Concat over time
# Actions chosen from replay buffer
central_chosen_action_qvals_agents = th.gather(
central_mac_out[:, :-1],
dim=3,
index=actions.unsqueeze(4).repeat(
1, 1, 1, 1, self.args.central_action_embed)).squeeze(
3) # Remove the last dim
central_target_mac_out = []
self.target_central_mac.init_hidden(batch.batch_size)
for t in range(batch.max_seq_length):
target_agent_outs = self.target_central_mac.forward(batch, t=t)
central_target_mac_out.append(target_agent_outs)
central_target_mac_out = th.stack(central_target_mac_out[:],
dim=1) # Concat across time
central_target_action_qvals_agents = th.gather(central_target_mac_out[:,:], 3, \
sampled_target_actions[:,:].unsqueeze(3).unsqueeze(4)\
.repeat(1,1,1,1,self.args.central_action_embed)).squeeze(3)
# ---
critic_bootstrap_qvals = self.target_central_mixer(
central_target_action_qvals_agents[:, 1:], batch["state"][:, 1:])
target_chosen_action_probs = th.gather(
target_mac_out, dim=3,
index=sampled_target_actions.unsqueeze(3)).squeeze(dim=3)
target_policy_logs = th.log(target_chosen_action_probs).sum(
dim=2, keepdim=True) # Sum across agents
# Calculate 1-step Q-Learning targets
targets = rewards + self.args.gamma * (1 - terminated) * \
(critic_bootstrap_qvals - self.args.entropy_temp * target_policy_logs[:,1:])
# Training Critic
central_chosen_action_qvals = self.central_mixer(
central_chosen_action_qvals_agents, batch["state"][:, :-1])
central_td_error = (central_chosen_action_qvals - targets.detach())
central_mask = mask.expand_as(central_td_error)
central_masked_td_error = central_td_error * central_mask
central_loss = (central_masked_td_error**2).sum() / mask.sum()
# Actor Loss
central_sampled_action_qvals_agents = th.gather(central_mac_out[:, :-1], 3, \
sampled_actions[:, :-1].unsqueeze(3).unsqueeze(4) \
.repeat(1, 1, 1, 1, self.args.central_action_embed)).squeeze(3)
central_sampled_action_qvals = self.central_mixer(
central_sampled_action_qvals_agents,
batch["state"][:, :-1]).repeat(1, 1, self.args.n_agents)
sampled_action_probs = th.gather(
mac_out, dim=3, index=sampled_actions.unsqueeze(3)).squeeze(3)
policy_logs = th.log(sampled_action_probs)[:, :-1]
actor_mask = mask.expand_as(policy_logs)
actor_loss = (
(policy_logs *
(self.args.entropy_temp *
(policy_logs + 1) - central_sampled_action_qvals).detach()) *
actor_mask).sum() / actor_mask.sum()
loss = self.args.actor_loss * actor_loss + self.args.central_loss * central_loss
# Optimise
self.optimiser.zero_grad()
loss.backward()
grad_norm = th.nn.utils.clip_grad_norm_(self.params,
self.args.grad_norm_clip)
self.grad_norm = grad_norm
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("actor_loss", actor_loss.item(), t_env)
self.logger.log_stat("grad_norm", grad_norm, t_env)
mask_elems = mask.sum().item()
self.logger.log_stat("target_mean", (targets * mask).sum().item() /
(mask_elems * self.args.n_agents), t_env)
self.logger.log_stat("central_loss", central_loss.item(), t_env)
ps = mac_out[:, :-1] * avail_actions[:, :-1]
log_ps = th.log(mac_out[:, :-1] + 0.00001) * avail_actions[:, :-1]
actor_entropy = -((
(ps * log_ps).sum(dim=3) * mask).sum() / mask.sum())
self.logger.log_stat("actor_entropy", actor_entropy.item(), 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())
if self.central_mac is not None:
self.target_central_mac.load_state(self.central_mac)
self.target_central_mixer.load_state_dict(
self.central_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()
if self.central_mac is not None:
self.central_mac.cuda()
self.target_central_mac.cuda()
self.central_mixer.cuda()
self.target_central_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)
# 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(
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))
class DDPGQLearner:
def __init__(self, mac, scheme, logger, args):
self.args = args
self.mac = mac
self.logger = logger
self.mac_params = list(mac.parameters())
self.params = list(self.mac.parameters())
self.last_target_update_episode = 0
self.mixer = None
assert args.mixer is not None
if args.mixer is not None:
if args.mixer == "vdn":
self.mixer = VDNMixer()
elif args.mixer == "qmix":
self.mixer = QMixer(args)
else:
raise ValueError("Mixer {} not recognised.".format(args.mixer))
self.mixer_params = list(self.mixer.parameters())
self.params += list(self.mixer.parameters())
self.target_mixer = copy.deepcopy(self.mixer)
# a little wasteful to deepcopy (e.g. duplicates action selector), but should work for any MAC
self.target_mac = copy.deepcopy(mac)
# Central Q
# TODO: Clean this mess up!
self.central_mac = None
assert self.args.central_mixer == "ff"
self.central_mixer = QMixerCentralFF(args)
assert args.central_mac == "basic_central_mac"
self.central_mac = mac_REGISTRY[args.central_mac](
scheme, args
) # Groups aren't used in the CentralBasicController. Little hacky
self.target_central_mac = copy.deepcopy(self.central_mac)
self.params += list(self.central_mac.parameters())
self.params += list(self.central_mixer.parameters())
self.target_central_mixer = copy.deepcopy(self.central_mixer)
self.optimiser = RMSprop(params=self.params,
lr=args.lr,
alpha=args.optim_alpha,
eps=args.optim_eps)
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"]
# 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_agents = th.gather(mac_out[:, :-1],
dim=3,
index=actions).squeeze(
3) # Remove the last dim
chosen_action_qvals = chosen_action_qvals_agents
# 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[:],
dim=1) # Concat across time
# Max over target Q-Values
if self.args.double_q:
raise Exception("No double q for DDPG")
else:
mac_out_detach = mac_out.clone().detach()
mac_out_detach[avail_actions == 0] = -9999999
_, cur_max_actions = mac_out_detach[:, :].max(dim=3, keepdim=True)
target_mac_out_detach = target_mac_out.clone().detach()
target_mac_out_detach[avail_actions == 0] = -9999999
_, tar_max_actions = target_mac_out_detach[:, :].max(dim=3,
keepdim=True)
# Central MAC stuff
central_mac_out = []
self.central_mac.init_hidden(batch.batch_size)
for t in range(batch.max_seq_length):
agent_outs = self.central_mac.forward(batch, t=t)
central_mac_out.append(agent_outs)
central_mac_out = th.stack(central_mac_out, dim=1) # Concat over time
central_chosen_action_qvals_agents = th.gather(
central_mac_out[:, :-1],
dim=3,
index=actions.unsqueeze(4).repeat(
1, 1, 1, 1, self.args.central_action_embed)).squeeze(
3) # Remove the last dim
central_target_mac_out = []
self.target_central_mac.init_hidden(batch.batch_size)
for t in range(batch.max_seq_length):
target_agent_outs = self.target_central_mac.forward(batch, t=t)
central_target_mac_out.append(target_agent_outs)
central_target_mac_out = th.stack(central_target_mac_out[:],
dim=1) # Concat across time
# Use the Qmix max actions
central_target_max_agent_qvals = th.gather(
central_target_mac_out[:, :], 3,
cur_max_actions[:, :].unsqueeze(4).repeat(
1, 1, 1, 1, self.args.central_action_embed)).squeeze(3)
# central_target_max_agent_qvals = th.gather(central_target_mac_out[:,:], 3, tar_max_actions[:,:].unsqueeze(4).repeat(1,1,1,1,self.args.central_action_embed)).squeeze(3)
# ---
# Mix
target_max_qvals = self.target_central_mixer(
central_target_max_agent_qvals[:, 1:], batch["state"][:, 1:])
# Calculate 1-step Q-Learning targets
targets = rewards + self.args.gamma * (1 -
terminated) * target_max_qvals
# Bad naming, its not a qmix_actor
qmix_actor_loss = 0
for agent in range(self.args.n_agents):
target_chosen_qvals = central_target_max_agent_qvals[:, :-1]
chosen_utils = target_chosen_qvals.detach().clone()
# For each agent compute Q(u_i, u_{-i}) for each u_i, keeping u_{-i} fixed
qtots = []
for action in range(self.args.n_actions):
chosen_utils[:, :,
agent] = central_target_mac_out[:, :-1, agent,
action]
new_q_tot = self.target_central_mixer(chosen_utils,
batch["state"][:, :-1])
qtots.append(new_q_tot)
agent_q_tots = th.cat(qtots, dim=2)
qs_to_use = agent_q_tots
# Train via ST Gumbel Softmax
log_agent_policy = F.gumbel_softmax(mac_out[:, :-1, agent],
hard=True,
dim=2,
tau=self.args.policy_temp)
# Train via expected policy gradient
# log_agent_policy = F.softmax(mac_out[:, :-1, agent] / self.args.policy_temp, dim=2)
agent_policy_loss = (
(log_agent_policy * qs_to_use.detach() *
avail_actions[:, :-1, agent].float()).sum(
dim=2, keepdim=True) * mask).sum() / mask.sum()
qmix_actor_loss = qmix_actor_loss + agent_policy_loss
# Logit entropy
ps = F.softmax(mac_out[:, :-1], dim=3) * avail_actions[:, :-1]
log_ps = F.log_softmax(mac_out[:, :-1], dim=3) * avail_actions[:, :-1]
logit_entropy = -(((ps * log_ps).sum(dim=3) * mask).sum() / mask.sum())
# Training central Q
central_chosen_action_qvals = self.central_mixer(
central_chosen_action_qvals_agents, batch["state"][:, :-1])
central_td_error = (central_chosen_action_qvals - targets.detach())
central_mask = mask.expand_as(central_td_error)
central_masked_td_error = central_td_error * central_mask
central_loss = (central_masked_td_error**2).sum() / mask.sum()
loss = -self.args.qmix_loss * qmix_actor_loss + self.args.central_loss * central_loss + -self.args.logit_entropy * logit_entropy
# Optimise
self.optimiser.zero_grad()
loss.backward()
grad_norm = th.nn.utils.clip_grad_norm_(self.params,
self.args.grad_norm_clip)
self.grad_norm = grad_norm
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("actor_loss", qmix_actor_loss.item(), t_env)
self.logger.log_stat("grad_norm", grad_norm, t_env)
mask_elems = mask.sum().item()
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.logger.log_stat("central_loss", central_loss.item(), t_env)
self.logger.log_stat("logit_entropy", logit_entropy.item(), 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())
if self.central_mac is not None:
self.target_central_mac.load_state(self.central_mac)
self.target_central_mixer.load_state_dict(
self.central_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()
if self.central_mac is not None:
self.central_mac.cuda()
self.target_central_mac.cuda()
self.central_mixer.cuda()
self.target_central_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)
# 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(
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))