def train_critic_batched(self, critic, target_critic, optimiser, batch, rewards, terminated, actions,
avail_actions, mask):
# Optimise critic
target_vals = target_critic(batch)
target_vals = target_vals[:, :-1]
if critic.output_type == 'q':
target_vals = th.gather(target_vals, dim=3, index=actions)
target_vals = th.cat([target_vals[:, 1:], th.zeros_like(target_vals[:, 0:1])], dim=1)
target_vals = target_vals.squeeze(3)
# Calculate td-lambda targets
targets = build_td_lambda_targets(rewards, terminated, mask, target_vals, self.n_agents,
self.args.gamma, self.args.td_lambda)
running_log = {
"critic_loss": [],
"critic_grad_norm": [],
"td_error_abs": [],
"target_mean": [],
"q_taken_mean": [],
}
all_vals = critic(batch)
vals = all_vals.clone()[:, :-1]
if critic.output_type == "q":
vals = th.gather(vals, dim=3, index=actions)
vals = vals.squeeze(3)
td_error = (vals - targets.detach())
# 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()
optimiser.zero_grad()
loss.backward()
grad_norm = th.nn.utils.clip_grad_norm_(optimiser.param_groups[0]["params"], self.args.grad_norm_clip)
optimiser.step()
self.critic_training_steps += 1
running_log["critic_loss"].append(loss.item())
running_log["critic_grad_norm"].append(grad_norm)
mask_elems = mask.sum().item()
running_log["td_error_abs"].append((masked_td_error.abs().sum().item() / mask_elems))
running_log["q_taken_mean"].append((vals * mask).sum().item() / mask_elems)
running_log["target_mean"].append((targets * mask).sum().item() / mask_elems)
if critic.output_type == 'q':
q_vals = all_vals[:, :-1]
v_s = None
else:
q_vals = build_td_lambda_targets(rewards, terminated, mask, all_vals.squeeze(3)[:, 1:], self.n_agents,
self.args.gamma, self.args.td_lambda)
v_s = vals
return q_vals, v_s, running_log
def _train_critic(self, batch, rewards, terminated, actions, avail_actions,
mask, bs, max_t):
# Optimise critic
target_q_vals = self.target_critic(batch)[:, :]
targets_taken = th.gather(target_q_vals, dim=3,
index=actions).squeeze(3)
# Calculate td-lambda targets
targets = build_td_lambda_targets(rewards, terminated, mask,
targets_taken, self.n_agents,
self.args.gamma, self.args.td_lambda)
q_vals = th.zeros_like(target_q_vals)[:, :-1]
running_log = {
"critic_loss": [],
"critic_grad_norm": [],
"td_error_abs": [],
"target_mean": [],
"q_taken_mean": [],
}
for t in reversed(range(rewards.size(1))):
mask_t = mask[:, t].expand(-1, self.n_agents)
if mask_t.sum() == 0:
continue
q_t = self.critic(batch, t)
q_vals[:, t] = q_t.view(bs, self.n_agents, self.n_actions)
q_taken = th.gather(q_t, dim=3,
index=actions[:,
t:t + 1]).squeeze(3).squeeze(1)
targets_t = targets[:, t]
td_error = (q_taken - targets_t.detach())
# 0-out the targets that came from padded data
masked_td_error = td_error * mask_t
# Normal L2 loss, take mean over actual data
loss = (masked_td_error**2).sum() / mask_t.sum()
self.critic_optimiser.zero_grad()
loss.backward()
grad_norm = th.nn.utils.clip_grad_norm_(self.critic_params,
self.args.grad_norm_clip)
self.critic_optimiser.step()
self.critic_training_steps += 1
running_log["critic_loss"].append(loss.item())
running_log["critic_grad_norm"].append(grad_norm)
mask_elems = mask_t.sum().item()
running_log["td_error_abs"].append(
(masked_td_error.abs().sum().item() / mask_elems))
running_log["q_taken_mean"].append(
(q_taken * mask_t).sum().item() / mask_elems)
running_log["target_mean"].append(
(targets_t * mask_t).sum().item() / mask_elems)
return q_vals, running_log
def _train_critic(self, batch, rewards, terminated, mask):
# Optimise critic
target_v_vals = self.target_critic(batch)[:, :]
v_vals = th.zeros_like(target_v_vals)
# Calculate td-lambda targets
target_v_vals = target_v_vals.squeeze(3)
targets = build_td_lambda_targets(rewards, terminated, mask,
target_v_vals, self.n_agents,
self.args.gamma, self.args.td_lambda)
running_log = {
"critic_loss": [],
"critic_grad_norm": [],
"td_error_abs": [],
"target_mean": [],
"q_taken_mean": [],
}
for t in reversed(range(rewards.size(1))):
v_t = self.critic(batch, t)
v_vals[:, t] = v_t.squeeze(1)
mask_t = mask[:, t]
if mask_t.sum() == 0:
continue
v_t = v_t.squeeze(3).squeeze(1)
targets_t = targets[:, t]
td_error = (v_t - targets_t.detach())
# 0-out the targets that came from padded data
masked_td_error = td_error * mask_t
# Normal L2 loss, take mean over actual data
loss = (masked_td_error**2).sum() / mask_t.sum()
self.critic_optimiser.zero_grad()
loss.backward()
grad_norm = th.nn.utils.clip_grad_norm_(self.critic_params,
self.args.grad_norm_clip)
self.critic_optimiser.step()
self.critic_training_steps += 1
running_log["critic_loss"].append(loss.item())
running_log["critic_grad_norm"].append(grad_norm)
mask_elems = mask_t.sum().item()
running_log["td_error_abs"].append(
(masked_td_error.abs().sum().item() / mask_elems))
running_log["q_taken_mean"].append(
(v_t * mask_t).sum().item() / mask_elems)
running_log["target_mean"].append(
(targets_t * mask_t).sum().item() / mask_elems)
qs = v_vals[:, :-1].squeeze(3)
vs = v_vals[:, :-1].squeeze(3)
return qs, vs, running_log
def train_critic_td(self, batch: EpisodeBatch, t_env: int, episode_num: int):
rewards = batch["reward"]
actions = batch["actions_onehot"]
terminated = batch["terminated"].float()
mask = batch["filled"].float()
mask[:, 1:] = mask[:, 1:] * (1 - terminated[:, :-1])
avail_actions = batch["avail_actions"]
# Optimise critic
target_q_vals = self.target_critic(actions, batch["state"])[:, :]
# Calculate td-lambda targets
targets = build_td_lambda_targets(rewards, terminated, mask, target_q_vals, self.n_agents, self.args.gamma, self.args.td_lambda)
running_log = {
"critic_loss": [],
"critic_grad_norm": [],
"td_error_abs": [],
"target_mean": [],
"q_t_mean": [],
}
mask = mask[:, :-1]
q_t = self.critic(actions[:, :-1], batch["state"][:, :-1])
td_error = (q_t - targets.detach())
# 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()
self.critic_optimiser.zero_grad()
loss.backward()
grad_norm = th.nn.utils.clip_grad_norm_(self.critic_params, self.args.grad_norm_clip)
self.critic_optimiser.step()
self.critic_training_steps += 1
running_log["critic_loss"].append(loss.item())
running_log["critic_grad_norm"].append(grad_norm)
mask_elems = mask.sum().item()
running_log["td_error_abs"].append((masked_td_error.abs().sum().item() / mask_elems))
running_log["q_t_mean"].append((q_t * mask).sum().item() / mask_elems)
running_log["target_mean"].append((targets * mask).sum().item() / mask_elems)
if t_env - self.log_stats_t >= self.args.learner_log_interval:
ts_logged = len(running_log["critic_loss"])
for key in ["critic_loss", "critic_grad_norm", "td_error_abs", "q_t_mean", "target_mean"]:
self.logger.log_stat(key, sum(running_log[key])/ts_logged, t_env)
self.log_stats_t = t_env
# Update target critic
if (self.critic_training_steps - self.last_target_update_episode) / self.args.target_update_interval >= 1.0:
self._update_targets()
self.last_target_update_episode = self.critic_training_steps
def train_critic(self, on_batch, best_batch=None, log=None):
bs = on_batch.batch_size
max_t = on_batch.max_seq_length
rewards = on_batch["reward"][:, :-1]
actions = on_batch["actions"][:, :]
terminated = on_batch["terminated"][:, :-1].float()
mask = on_batch["filled"][:, :-1].float()
mask[:, 1:] = mask[:, 1:] * (1 - terminated[:, :-1])
#build_target_q
target_inputs = self.target_critic._build_inputs(on_batch, bs, max_t)
target_q_vals = self.target_critic.forward(target_inputs).detach()
targets_taken = th.mean(th.gather(target_q_vals, dim=3, index=actions).squeeze(3), dim=2, keepdim=True)
target_q = build_td_lambda_targets(rewards, terminated, mask, targets_taken, self.n_agents, self.args.gamma, self.args.td_lambda).repeat(1, 1, self.n_agents)
inputs = self.critic._build_inputs(on_batch, bs, max_t)
if best_batch is not None:
best_target_q, best_inputs, best_mask, best_actions= self.train_critic_best(best_batch)
target_q = th.cat((target_q, best_target_q), dim=0)
inputs = th.cat((inputs, best_inputs), dim=0)
mask = th.cat((mask, best_mask), dim=0)
actions = th.cat((actions, best_actions), dim=0)
mask = mask.repeat(1, 1, self.n_agents)
#train critic
for t in range(max_t - 1):
mask_t = mask[:, t:t+1]
if mask_t.sum() < 0.5:
continue
q_vals = self.critic.forward(inputs[:, t:t+1])
q_vals = th.gather(q_vals, 3, index=actions[:, t:t+1]).squeeze(3)
target_q_t = target_q[:, t:t+1]
q_err = (q_vals - target_q_t) * mask_t
critic_loss = (q_err ** 2).sum() / mask_t.sum()
self.critic_optimiser.zero_grad()
critic_loss.backward()
grad_norm = th.nn.utils.clip_grad_norm_(self.critic_params, self.args.grad_norm_clip)
self.critic_optimiser.step()
self.critic_training_steps += 1
log["critic_loss"].append(critic_loss.item())
log["critic_grad_norm"].append(grad_norm)
mask_elems = mask_t.sum().item()
log["td_error_abs"].append((q_err.abs().sum().item() / mask_elems))
log["target_mean"].append((target_q_t * mask_t).sum().item() / mask_elems)
log["q_taken_mean"].append((q_vals * mask_t).sum().item() / mask_elems)
#update target network
if (self.critic_training_steps - self.last_target_update_step) / self.args.target_update_interval >= 1.0:
self._update_targets()
self.last_target_update_step = self.critic_training_steps
def _train_critic(self, batch, rewards, terminated, actions, avail_actions, mask, bs, max_t):
# Optimise critic
r_in, target_vals, target_val_ex = self.target_critic(batch)
r_in, _, target_val_ex_opt = self.critic(batch)
r_in_taken = th.gather(r_in, dim=3, index=actions)
r_in = r_in_taken.squeeze(-1)
target_vals = target_vals.squeeze(-1)
targets_mix, targets_ex = build_td_lambda_targets(rewards, terminated, mask, target_vals, self.n_agents,
self.args.gamma, self.args.td_lambda, r_in, target_val_ex)
vals_mix = th.zeros_like(target_vals)[:, :-1]
vals_ex = target_val_ex_opt[:, :-1]
running_log = {
"critic_loss": [],
"critic_grad_norm": [],
"td_error_abs": [],
"target_mean": [],
"value_mean": [],
}
for t in reversed(range(rewards.size(1))):
mask_t = mask[:, t].expand(-1, self.n_agents)
if mask_t.sum() == 0:
continue
_, q_t, _ = self.critic(batch, t) # 8,1,3,1,
vals_mix[:, t] = q_t.view(bs, self.n_agents)
targets_t = targets_mix[:, t]
td_error = (q_t.view(bs, self.n_agents) - targets_t.detach())
# 0-out the targets that came from padded data
masked_td_error = td_error * mask_t
# Normal L2 loss, take mean over actual data
loss = (masked_td_error ** 2).sum() / mask_t.sum()
self.critic_optimiser.zero_grad()
loss.backward()
grad_norm = th.nn.utils.clip_grad_norm_(self.critic_params, self.args.grad_norm_clip)
self.critic_optimiser.step()
self.critic_training_steps += 1
running_log["critic_loss"].append(loss.item())
running_log["critic_grad_norm"].append(grad_norm)
mask_elems = mask_t.sum().item()
running_log["td_error_abs"].append((masked_td_error.abs().sum().item() / mask_elems))
running_log["value_mean"].append((q_t.view(bs, self.n_agents) * mask_t).sum().item() / mask_elems)
running_log["target_mean"].append((targets_t * mask_t).sum().item() / mask_elems)
return vals_mix, running_log, targets_mix, targets_ex, vals_ex, r_in
def _calculate_advs(self, batch, rewards, terminated, actions,
avail_actions, mask, bs, max_t):
mac_out = []
q_outs = []
# Roll out experiences
self.mac.init_hidden(batch.batch_size)
for t in range(batch.max_seq_length):
agent_out, q_out = self.mac.forward(batch, t=t)
mac_out.append(agent_out)
q_outs.append(q_out)
mac_out = th.stack(mac_out, dim=1) # Concat over time
q_outs = th.stack(q_outs, 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
# Calculated baseline
pi = mac_out[:, :-1] #[bs, t, n_agents, n_actions]
pi_taken = th.gather(pi, dim=-1, index=actions[:, :-1]).squeeze(
-1) #[bs, t, n_agents]
action_mask = mask.repeat(1, 1, self.n_agents)
pi_taken[action_mask == 0] = 1.0
log_pi_taken = th.log(pi_taken).reshape(-1)
# Calculate entropy
entropy = categorical_entropy(pi).reshape(-1) #[bs, t, n_agents, 1]
# Calculate q targets
targets_taken = q_outs.squeeze(-1) #[bs, t, n_agents]
if self.args.mixer:
targets_taken = self.mixer(targets_taken,
batch["state"][:, :]) #[bs, t, 1]
# Calculate td-lambda targets
targets = build_td_lambda_targets(rewards, terminated, mask,
targets_taken, self.n_agents,
self.args.gamma, self.args.td_lambda)
advantages = targets - targets_taken[:, :-1]
advantages = advantages.unsqueeze(2).repeat(1, 1, self.n_agents,
1).reshape(-1)
td_error = targets_taken[:, :-1] - targets.detach()
td_error = td_error.unsqueeze(2).repeat(1, 1, self.n_agents,
1).reshape(-1)
return advantages, td_error, targets_taken[:, :-1].unsqueeze(2).repeat(
1, 1, self.n_agents, 1).reshape(-1), log_pi_taken, entropy
def train_critic(self, on_batch, best_batch=None, log=None):
bs = on_batch.batch_size
max_t = on_batch.max_seq_length
rewards = on_batch["reward"][:, :-1]
actions = on_batch["actions"][:, :]
terminated = on_batch["terminated"][:, :-1].float()
mask = on_batch["filled"][:, :-1].float()
mask[:, 1:] = mask[:, 1:] * (1 - terminated[:, :-1])
avail_actions = on_batch["avail_actions"][:]
states = on_batch["state"]
#build_target_q
target_inputs = self.target_critic._build_inputs(on_batch, bs, max_t)
target_q_vals = self.target_critic.forward(target_inputs).detach()
targets_taken = self.target_mixer(
th.gather(target_q_vals, dim=3, index=actions).squeeze(3), states)
target_q = build_td_lambda_targets(rewards, terminated, mask,
targets_taken, self.n_agents,
self.args.gamma,
self.args.td_lambda).detach()
inputs = self.critic._build_inputs(on_batch, bs, max_t)
mac_out = []
self.mac.init_hidden(bs)
for i in range(max_t):
agent_outs = self.mac.forward(on_batch, t=i)
mac_out.append(agent_outs)
mac_out = th.stack(mac_out, dim=1).detach()
# 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
if best_batch is not None:
best_target_q, best_inputs, best_mask, best_actions, best_mac_out = self.train_critic_best(
best_batch)
log["best_reward"] = th.mean(
best_batch["reward"][:, :-1].squeeze(2).sum(-1), dim=0)
target_q = th.cat((target_q, best_target_q), dim=0)
inputs = th.cat((inputs, best_inputs), dim=0)
mask = th.cat((mask, best_mask), dim=0)
actions = th.cat((actions, best_actions), dim=0)
states = th.cat((states, best_batch["state"]), dim=0)
mac_out = th.cat((mac_out, best_mac_out), dim=0)
#train critic
mac_out = mac_out.detach()
for t in range(max_t - 1):
mask_t = mask[:, t:t + 1]
if mask_t.sum() < 0.5:
continue
k = self.mixer.k(states[:, t:t + 1]).unsqueeze(3)
#b = self.mixer.b(states[:, t:t+1])
q_vals = self.critic.forward(inputs[:, t:t + 1])
q_ori = q_vals
q_vals = th.gather(q_vals, 3, index=actions[:, t:t + 1]).squeeze(3)
q_vals = self.mixer.forward(q_vals, states[:, t:t + 1])
target_q_t = target_q[:, t:t + 1].detach()
q_err = (q_vals - target_q_t) * mask_t
critic_loss = (q_err**2).sum() / mask_t.sum()
#Here introduce the loss for Qi
v_vals = th.sum(q_ori * mac_out[:, t:t + 1], dim=3, keepdim=True)
ad_vals = q_ori - v_vals
goal = th.sum(k * v_vals, dim=2, keepdim=True) + k * ad_vals
goal_err = (goal - q_ori) * mask_t
goal_loss = 0.1 * (goal_err**
2).sum() / mask_t.sum() / self.args.n_actions
#critic_loss += goal_loss
self.critic_optimiser.zero_grad()
self.mixer_optimiser.zero_grad()
critic_loss.backward()
grad_norm = th.nn.utils.clip_grad_norm_(self.c_params,
self.args.grad_norm_clip)
self.critic_optimiser.step()
self.mixer_optimiser.step()
self.critic_training_steps += 1
log["critic_loss"].append(critic_loss.item())
log["critic_grad_norm"].append(grad_norm)
mask_elems = mask_t.sum().item()
log["td_error_abs"].append((q_err.abs().sum().item() / mask_elems))
log["target_mean"].append(
(target_q_t * mask_t).sum().item() / mask_elems)
log["q_taken_mean"].append(
(q_vals * mask_t).sum().item() / mask_elems)
log["q_max_mean"].append(
(th.mean(q_ori.max(dim=3)[0], dim=2, keepdim=True) *
mask_t).sum().item() / mask_elems)
log["q_min_mean"].append(
(th.mean(q_ori.min(dim=3)[0], dim=2, keepdim=True) *
mask_t).sum().item() / mask_elems)
log["q_max_var"].append(
(th.var(q_ori.max(dim=3)[0], dim=2, keepdim=True) *
mask_t).sum().item() / mask_elems)
log["q_min_var"].append(
(th.var(q_ori.min(dim=3)[0], dim=2, keepdim=True) *
mask_t).sum().item() / mask_elems)
if (t == 0):
log["q_max_first"] = (
th.mean(q_ori.max(dim=3)[0], dim=2, keepdim=True) *
mask_t).sum().item() / mask_elems
log["q_min_first"] = (
th.mean(q_ori.min(dim=3)[0], dim=2, keepdim=True) *
mask_t).sum().item() / mask_elems
#update target network
if (self.critic_training_steps - self.last_target_update_step
) / self.args.target_update_interval >= 1.0:
self._update_targets()
self.last_target_update_step = self.critic_training_steps
def train(self,
batch: EpisodeBatch,
t_env: int,
episode_num: int,
epsilon_levin=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 = []
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)
# Calculate the Q-Values necessary for the target
target_mac_out = []
if not self.args.SubAVG_Agent_flag:
self.target_mac.init_hidden(batch.batch_size)
else:
for i in range(self.args.SubAVG_Agent_K):
self.target_mac_list[i].init_hidden(batch.batch_size)
for t in range(batch.max_seq_length):
if not self.args.SubAVG_Agent_flag:
target_agent_outs = self.target_mac.forward(batch, t=t)
# exp:使用 average DQN的target_mac
else:
target_agent_outs = 0
self.target_agent_out_list = []
for i in range(self.args.SubAVG_Agent_K):
target_agent_out = self.target_mac_list[i].forward(batch,
t=t)
target_agent_outs = target_agent_outs + target_agent_out
if self.args.SubAVG_Agent_flag_select:
self.target_agent_out_list.append(target_agent_out)
target_agent_outs = target_agent_outs / self.args.SubAVG_Agent_K
if self.args.SubAVG_Agent_flag_select:
if self.args.SubAVG_Agent_name_select_replacement == 'mean':
target_out_select_sum = 0
for i in range(self.args.SubAVG_Agent_K):
if self.args.SubAVG_Agent_flag_select > 0:
target_out_select = th.where(
self.target_agent_out_list[i] <
target_agent_outs, target_agent_outs,
self.target_agent_out_list[i])
else:
target_out_select = th.where(
self.target_agent_out_list[i] >
target_agent_outs, target_agent_outs,
self.target_agent_out_list[i])
target_out_select_sum = target_out_select_sum + target_out_select
target_agent_outs = target_out_select_sum / self.args.SubAVG_Agent_K
elif self.args.SubAVG_Agent_name_select_replacement == 'zero':
target_out_select_sum = 0
target_select_bool_sum = 0
for i in range(self.args.SubAVG_Agent_K):
if self.args.SubAVG_Agent_flag_select > 0:
target_select_bool = (
self.target_agent_out_list[i] >
target_agent_outs).float()
target_out_select = th.where(
self.target_agent_out_list[i] >
target_agent_outs,
self.target_agent_out_list[i],
th.full_like(target_agent_outs, 0))
else:
target_select_bool = (
self.target_agent_out_list[i] <
target_agent_outs).float()
target_out_select = th.where(
self.target_agent_out_list[i] <
target_agent_outs,
self.target_agent_out_list[i],
th.full_like(target_agent_outs, 0))
target_select_bool_sum = target_select_bool_sum + target_select_bool
target_out_select_sum = target_out_select_sum + target_out_select
if self.levin_iter_target_update < 2:
pass # print("using average directly")
else:
target_agent_outs = target_out_select_sum / target_select_bool_sum
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_chosen_action_qvals = th.gather(target_mac_out, 3,
batch['actions']).squeeze(-1)
# Mix
if self.mixer is None:
target_qvals = target_chosen_action_qvals
else:
chosen_action_qvals = self.mixer(chosen_action_qvals,
batch["state"][:, :-1])
if not self.args.SubAVG_Mixer_flag:
target_qvals = self.target_mixer(target_chosen_action_qvals,
batch['state'])
elif self.args.mixer == "qmix":
target_max_qvals_sum = 0
self.target_mixer_out_list = []
for i in range(self.args.SubAVG_Mixer_K):
targe_mixer_out = self.target_mixer_list[i](
target_chosen_action_qvals, batch['state'])
target_max_qvals_sum = target_max_qvals_sum + targe_mixer_out
if self.args.SubAVG_Mixer_flag_select:
self.target_mixer_out_list.append(targe_mixer_out)
target_max_qvals = target_max_qvals_sum / self.args.SubAVG_Mixer_K
# levin: mixer select
if self.args.SubAVG_Mixer_flag_select:
if self.args.SubAVG_Mixer_name_select_replacement == 'mean':
target_mixer_select_sum = 0
for i in range(self.args.SubAVG_Mixer_K):
if self.args.SubAVG_Mixer_flag_select > 0:
target_mixer_select = th.where(
self.target_mixer_out_list[i] <
target_max_qvals, target_max_qvals,
self.target_mixer_out_list[i])
else:
target_mixer_select = th.where(
self.target_mixer_out_list[i] >
target_max_qvals, target_max_qvals,
self.target_mixer_out_list[i])
target_mixer_select_sum = target_mixer_select_sum + target_mixer_select
target_max_qvals = target_mixer_select_sum / self.args.SubAVG_Mixer_K
elif self.args.SubAVG_Mixer_name_select_replacement == 'zero':
target_mixer_select_sum = 0
target_mixer_select_bool_sum = 0
for i in range(self.args.SubAVG_Mixer_K):
if self.args.SubAVG_Mixer_flag_select > 0:
target_mixer_select_bool = (
self.target_mixer_out_list[i] >
target_max_qvals).float()
target_mixer_select = th.where(
self.target_mixer_out_list[i] >
target_max_qvals,
self.target_mixer_out_list[i],
th.full_like(target_max_qvals, 0))
else:
target_mixer_select_bool = (
self.target_mixer_out_list[i] <
target_max_qvals).float()
target_mixer_select = th.where(
self.target_mixer_out_list[i] <
target_max_qvals,
self.target_mixer_out_list[i],
th.full_like(target_max_qvals, 0))
target_mixer_select_bool_sum = target_mixer_select_bool_sum + target_mixer_select_bool
target_mixer_select_sum = target_mixer_select_sum + target_mixer_select
if self.levin_iter_target_mixer_update < 2:
pass # print("using average-mix directly")
else:
target_max_qvals = target_mixer_select_sum / target_mixer_select_bool_sum
target_qvals = target_max_qvals
if self.args.td_lambda <= 1 and self.args.td_lambda > 0:
targets = build_td_lambda_targets(rewards, terminated, mask,
target_qvals, self.args.n_agents,
self.args.gamma,
self.args.td_lambda)
else:
if self.args.td_lambda == 0:
n = 1 # 1-step TD
else:
n = self.args.td_lambda
targets = th.zeros_like(batch['reward'])
targets += batch['reward']
for i in range(1, n):
targets[:, :-i] += (self.args.gamma**i) * (
1 - terminated[:, i - 1:]) * batch['reward'][:, i:]
targets[:, :-n] += (self.args.gamma**n) * (
1 - terminated[:, n - 1:]) * target_qvals[:, n:]
targets = targets[:, :-1]
# 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() * 2
# 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("loss_levin", loss_levin.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 train_critic_sequential(self, critic, target_critic, optimiser, batch, rewards, terminated, actions,
avail_actions, mask):
# Optimise critic
target_vals = target_critic(batch)
all_vals = th.zeros_like(target_vals)
if critic.output_type == 'q':
target_vals = th.gather(target_vals, dim=3, index=actions)
# target_vals = th.cat([target_vals[:, 1:], th.zeros_like(target_vals[:, 0:1])], dim=1)
target_vals = target_vals.squeeze(3)
# Calculate td-lambda targets
targets = build_td_lambda_targets(rewards, terminated, mask, target_vals, self.n_agents,
self.args.gamma, self.args.td_lambda)
running_log = {
"critic_loss": [],
"critic_grad_norm": [],
"td_error_abs": [],
"target_mean": [],
"q_taken_mean": [],
}
for t in reversed(range(rewards.size(1) + 1)):
vals_t = critic(batch, t)
all_vals[:, t] = vals_t.squeeze(1)
if t == rewards.size(1):
continue
mask_t = mask[:, t]
if mask_t.sum() == 0:
continue
if critic.output_type == "q":
vals_t = th.gather(vals_t, dim=3, index=actions[:, t:t+1]).squeeze(3).squeeze(1)
else:
vals_t = vals_t.squeeze(3).squeeze(1)
targets_t = targets[:, t]
td_error = (vals_t - targets_t.detach())
# 0-out the targets that came from padded data
masked_td_error = td_error * mask_t
# Normal L2 loss, take mean over actual data
loss = (masked_td_error ** 2).sum() / mask_t.sum() # Not dividing by number of agents, only # valid timesteps
optimiser.zero_grad()
loss.backward()
grad_norm = th.nn.utils.clip_grad_norm_(optimiser.param_groups[0]["params"], self.args.grad_norm_clip)
optimiser.step()
self.critic_training_steps += 1
running_log["critic_loss"].append(loss.item())
running_log["critic_grad_norm"].append(grad_norm)
mask_elems = mask_t.sum().item()
running_log["td_error_abs"].append((masked_td_error.abs().sum().item() / mask_elems))
running_log["q_taken_mean"].append((vals_t * mask_t).sum().item() / mask_elems)
running_log["target_mean"].append((targets_t * mask_t).sum().item() / mask_elems)
if critic.output_type == 'q':
q_vals = all_vals[:, :-1]
v_s = None
else:
q_vals = all_vals[:, :-1].squeeze(3)
v_s = all_vals[:, :-1].squeeze(3)
return q_vals, v_s, running_log
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 train(self,
batch: EpisodeBatch,
t_env: int,
episode_num: int,
off=False):
# Get the relevant data
rewards = batch["reward"][:, :-1]
actions = batch["actions"][:, :-1]
actions_onehot = batch["actions_onehot"][:, :-1]
terminated = batch["terminated"][:, :-1].float()
mask = batch["filled"][:, :-1].float()
mask[:, 1:] = mask[:, 1:] * (1 - terminated[:, :-1])
avail_actions = batch["avail_actions"]
# Retrace Q target
with th.no_grad():
q1, _ = self.target_critic(batch, batch["actions_onehot"].detach())
target_vals = self.target_mixer(q1, batch["state"])
lambd = 0 if off else self.args.lambd
target_vals = build_td_lambda_targets(rewards, terminated, mask,
target_vals, self.n_agents,
self.args.gamma, lambd)
# Train the critic
# Current Q network forward
q1, _ = self.critic(batch[:, :-1], actions_onehot.detach())
q_taken = self.mixer(q1, batch["state"][:, :-1])
critic_loss = 0.5 * (
(q_taken - target_vals.detach()) * mask).pow(2).sum() / mask.sum()
self.critic_optimiser.zero_grad()
critic_loss.backward()
critic_grad_norm = th.nn.utils.clip_grad_norm_(
self.critic_params, self.args.grad_norm_clip)
self.critic_optimiser.step()
# Train the actor
if not off:
pi = []
self.mac.init_hidden(batch.batch_size)
for t in range(batch.max_seq_length - 1):
agent_outs = self.mac.forward(batch, t=t)
pi.append(agent_outs)
pi = th.stack(pi, dim=1) # Concat over time b, t, a, probs
q1, _ = self.critic(batch[:, :-1], pi)
q = self.mixer(q1, batch["state"][:, :-1])
pg_loss = -(q * mask).sum() / mask.sum()
entropy_loss = categorical_entropy(pi).mean(
-1, keepdim=True) # mean over agents
entropy_loss[mask == 0] = 0 # fill nan
entropy_loss = (entropy_loss * mask).sum() / mask.sum()
loss = pg_loss - self.args.entropy_coef * entropy_loss / entropy_loss.item(
)
self.agent_optimiser.zero_grad()
loss.backward()
agent_grad_norm = th.nn.utils.clip_grad_norm_(
self.agent_params, self.args.grad_norm_clip)
self.agent_optimiser.step()
# target_update
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
# log
if t_env - self.log_stats_t >= self.args.learner_log_interval:
self.logger.log_stat("critic_loss", critic_loss.item(), t_env)
self.logger.log_stat("critic_grad_norm", critic_grad_norm.item(),
t_env)
self.logger.log_stat("target_vals",
(target_vals * mask).sum().item() /
mask.sum().item(), t_env)
if not off:
self.logger.log_stat("pg_loss", pg_loss.item(), t_env)
self.logger.log_stat("entropy_loss", entropy_loss.item(),
t_env)
self.logger.log_stat("agent_grad_norm", agent_grad_norm.item(),
t_env)
agent_mask = mask.repeat(1, 1, self.n_agents)
self.logger.log_stat(
"pi_max", (pi.max(dim=-1)[0] * agent_mask).sum().item() /
agent_mask.sum().item(), t_env)
self.log_stats_t = t_env
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 = th.gather(mac_out[:, :-1], dim=3,
index=actions).squeeze(
3) # Remove the last dim
chosen_action_qvals_ = chosen_action_qvals
# Calculate the Q-Values necessary for the target
with th.no_grad():
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/ Double q learning
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)[1]
target_max_qvals = th.gather(target_mac_out, 3,
cur_max_actions).squeeze(3)
# Calculate n-step Q-Learning targets
target_max_qvals = self.target_mixer(target_max_qvals,
batch["state"])
if getattr(self.args, 'q_lambda', False):
qvals = th.gather(target_mac_out, 3,
batch["actions"]).squeeze(3)
qvals = self.target_mixer(qvals, batch["state"])
targets = build_q_lambda_targets(rewards, terminated, mask,
target_max_qvals, qvals,
self.args.gamma,
self.args.td_lambda)
else:
targets = build_td_lambda_targets(rewards, terminated, mask,
target_max_qvals,
self.args.n_agents,
self.args.gamma,
self.args.td_lambda)
# Mixer
chosen_action_qvals = self.mixer(chosen_action_qvals,
batch["state"][:, :-1])
td_error = (chosen_action_qvals - targets.detach())
td_error = 0.5 * td_error.pow(2)
mask = mask.expand_as(td_error)
masked_td_error = td_error * mask
loss = L_td = masked_td_error.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_td", L_td.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
# print estimated matrix
if self.args.env == "one_step_matrix_game":
print_matrix_status(batch, self.mixer, mac_out)
def _train_critic(self, batch, rewards, terminated, actions, avail_actions,
mask, bs, max_t):
# Optimise critic
target_q_vals = []
for idx, target_critic_ in enumerate(self.target_critic):
target_q_vals.append(target_critic_(batch)[:, :])
targets_taken = [
th.gather(target_q_vals_, dim=3, index=actions_).squeeze(3)
for target_q_vals_, actions_ in zip(target_q_vals, actions)
]
# Calculate td-lambda targets
targets = [
build_td_lambda_targets(rewards_, terminated, mask,
targets_taken_, n_agents_,
self.args.gamma,
self.args.td_lambda)
for rewards_, targets_taken_, n_agents_
in zip(rewards, targets_taken, self.n_agents)
]
q_vals = [th.zeros_like(target_q_vals_)[:, :-1]
for target_q_vals_ in target_q_vals]
running_log = {
"critic_loss_1": [],
"critic_loss_2": [],
"critic_grad_norm_1": [],
"critic_grad_norm_2": [],
"td_error_abs_1": [],
"td_error_abs_2": [],
"target_mean_1": [],
"target_mean_2": [],
"q_taken_mean_1": [],
"q_taken_mean_2": [],
}
for idx, (rewards_, n_agents_, critic_, targets_,
critic_optimiser_, critic_params_) in enumerate(
zip(rewards, self.n_agents, self.critic, targets,
self.critic_optimiser, self.critic_params)):
for t in reversed(range(rewards_.size(1))):
mask_t = mask[:, t].expand(-1, n_agents_)
if mask_t.sum() == 0:
continue
q_t = critic_(batch, t)
q_vals[idx][:, t] = q_t.view(bs, n_agents_, self.n_actions)
q_taken = th.gather(q_t, dim=3,
index=actions[idx][:, t:t + 1]).squeeze(
3).squeeze(1)
targets_t = targets_[:, t]
td_error = (q_taken - targets_t.detach())
# 0-out the targets that came from padded data
masked_td_error = td_error * mask_t
# Normal L2 loss, take mean over actual data
loss = (masked_td_error ** 2).sum() / mask_t.sum()
critic_optimiser_.zero_grad()
loss.backward()
grad_norm = th.nn.utils.clip_grad_norm_(critic_params_,
self.args.grad_norm_clip)
critic_optimiser_.step()
self.critic_training_steps += 1
running_log["critic_loss_" + str(idx + 1)].append(loss.item())
running_log["critic_grad_norm_" + str(idx + 1)].append(
grad_norm)
mask_elems = mask_t.sum().item()
running_log["td_error_abs_" + str(idx + 1)].append(
(masked_td_error.abs().sum().item() / mask_elems))
running_log["q_taken_mean_" + str(idx + 1)].append(
(q_taken * mask_t).sum().item() / mask_elems)
running_log["target_mean_" + str(idx + 1)].append(
(targets_t * mask_t).sum().item() / mask_elems)
return q_vals, running_log
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 = 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, dim=1) # Concat across time
target_chosen_action_qvals = th.gather(target_mac_out,
3, batch['actions']).squeeze(
-1) # [32, 52, 3]
if self.mixer is None:
target_qvals = target_chosen_action_qvals
else:
target_qvals = self.target_mixer(target_chosen_action_qvals,
batch['state']) # [32, 52, 1]
chosen_action_qvals = self.mixer(chosen_action_qvals,
batch["state"][:, :-1])
if self.args.td_lambda <= 1 and self.args.td_lambda > 0: # (0,1] for td-lambda
targets = build_td_lambda_targets(
rewards, terminated, mask, target_qvals, self.args.n_agents,
self.args.gamma, self.args.td_lambda) # [32, 51, 1]
else:
if self.args.td_lambda == 0:
n = 1 # 1-step TD
else:
n = self.args.td_lambda
targets = th.zeros_like(batch['reward'])
targets += batch['reward']
for i in range(1, n):
targets[:, :-i] += (self.args.gamma**i) * (
1 - terminated[:, i - 1:]) * batch['reward'][:, i:]
targets[:, :-n] += (self.args.gamma**n) * (
1 - terminated[:, n - 1:]) * target_qvals[:, n:]
targets = targets[:, :-1]
# 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()
# 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 sub_train(self,
batch: EpisodeBatch,
t_env: int,
episode_num: int,
mac,
mixer,
optimiser,
params,
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"]
actions_onehot = batch["actions_onehot"][:, :-1]
# Calculate estimated Q-Values
mac_out = []
mac.init_hidden(batch.batch_size)
for t in range(batch.max_seq_length):
agent_outs = 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
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()
# self.logger.log_stat('agent_1_%d_q_1' % save_data[0], np.squeeze(q_data)[0], t_env)
# self.logger.log_stat('agent_2_%d_q_2' % save_data[1], np.squeeze(q_data)[1], t_env)
# 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
# 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.max(dim=3, keepdim=True)[1]
target_chosen_qvals = th.gather(target_mac_out, 3,
cur_max_actions).squeeze(3)
target_max_qvals = target_mac_out.max(dim=3)[0]
cur_max_actions_onehot = th.zeros(
cur_max_actions.squeeze(3).shape + (self.n_actions, )).cuda()
cur_max_actions_onehot = cur_max_actions_onehot.scatter_(
3, cur_max_actions, 1)
else:
raise "Use Double Q"
# Mix
if mixer is not None:
ans_chosen = mixer(chosen_action_qvals,
batch["state"][:, :-1],
is_v=True)
ans_adv = mixer(chosen_action_qvals,
batch["state"][:, :-1],
actions=actions_onehot,
max_q_i=max_action_qvals,
is_v=False)
chosen_action_qvals = ans_chosen + ans_adv
if self.args.double_q:
target_chosen = self.target_mixer(target_chosen_qvals,
batch["state"],
is_v=True)
target_adv = self.target_mixer(target_chosen_qvals,
batch["state"],
actions=cur_max_actions_onehot,
max_q_i=target_max_qvals,
is_v=False)
target_max_qvals = target_chosen + target_adv
else:
raise "Use Double Q"
# Calculate 1-step Q-Learning targets
targets = build_td_lambda_targets(rewards, terminated, mask,
target_max_qvals, self.args.n_agents,
self.args.gamma, self.args.td_lambda)
if show_demo:
tot_q_data = chosen_action_qvals.detach().cpu().numpy()
tot_target = targets.detach().cpu().numpy()
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
# 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 = 0.5 * (masked_td_error**2).sum() / mask.sum()
masked_hit_prob = th.mean(is_max_action, dim=2) * mask
hit_prob = masked_hit_prob.sum() / mask.sum()
# Optimise
optimiser.zero_grad()
loss.backward()
grad_norm = th.nn.utils.clip_grad_norm_(params,
self.args.grad_norm_clip)
optimiser.step()
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("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
