class QMIX:
def __init__(self, args):
self.n_actions = args.n_actions
self.n_agents = args.n_agents
self.state_shape = args.state_shape
self.obs_shape = args.obs_shape
input_shape = self.obs_shape
real_view_shape = args.real_view_shape
input_shape_view = args.view_shape
input_shape_feature = args.feature_shape
# 根据参数决定RNN的输入维度
if args.last_action:
input_shape += self.n_actions
if args.reuse_network:
input_shape += self.n_agents
# 神经网络
# self.eval_rnn = RNN(input_shape, args) # 每个agent选动作的网络
# self.target_rnn = RNN(input_shape, args)
# print(real_view_shape, input_shape_view, input_shape_feature)
self.eval_rnn = ConvNet_MLP(real_view_shape, input_shape_view, input_shape_feature, args) # 每个agent选动作的网络
self.target_rnn = ConvNet_MLP(real_view_shape, input_shape_view, input_shape_feature, args)
self.eval_qmix_net = QMixNet(args) # 把agentsQ值加起来的网络
self.target_qmix_net = QMixNet(args)
self.args = args
if self.args.cuda:
self.eval_rnn.cuda()
self.target_rnn.cuda()
self.eval_qmix_net.cuda()
self.target_qmix_net.cuda()
self.model_dir = args.model_dir + '/' + args.alg + '/' + args.env_name + '/' + str(args.map_size)
# print(self.model_dir)
# 如果存在模型则加载模型
if self.args.load_model:
if os.path.exists(self.model_dir + '/rnn_net_params.pkl'):
path_rnn = self.model_dir + '/rnn_net_params.pkl'
path_qmix = self.model_dir + '/qmix_net_params.pkl'
map_location = 'cuda:0' if self.args.cuda else 'cpu'
self.eval_rnn.load_state_dict(torch.load(path_rnn, map_location=map_location))
# self.eval_qmix_net.load_state_dict(torch.load(path_qmix, map_location=map_location))
print('Successfully load the model: {} and {}'.format(path_rnn, path_qmix))
else:
raise Exception("No model!")
# 让target_net和eval_net的网络参数相同
self.target_rnn.load_state_dict(self.eval_rnn.state_dict())
self.target_qmix_net.load_state_dict(self.eval_qmix_net.state_dict())
self.eval_parameters = list(self.eval_qmix_net.parameters()) + list(self.eval_rnn.parameters())
if args.optimizer == "RMS":
self.optimizer = torch.optim.RMSprop(self.eval_parameters, lr=args.lr)
# 执行过程中，要为每个agent都维护一个eval_hidden
# 学习过程中，要为每个episode的每个agent都维护一个eval_hidden、target_hidden
self.eval_hidden = None
self.target_hidden = None
print('Init alg QMIX')
def learn(self, batch, max_episode_len, train_step, epsilon=None): # train_step表示是第几次学习，用来控制更新target_net网络的参数
'''
在learn的时候，抽取到的数据是四维的，四个维度分别为 1——第几个episode 2——episode中第几个transition
3——第几个agent的数据 4——具体obs维度。因为在选动作时不仅需要输入当前的inputs，还要给神经网络输入hidden_state，
hidden_state和之前的经验相关，因此就不能随机抽取经验进行学习。所以这里一次抽取多个episode，然后一次给神经网络
传入每个episode的同一个位置的transition
'''
episode_num = batch['o'].shape[0]
self.init_hidden(episode_num)
for key in batch.keys(): # 把batch里的数据转化成tensor
if key == 'u':
batch[key] = torch.tensor(batch[key], dtype=torch.long)
else:
batch[key] = torch.tensor(batch[key], dtype=torch.float32)
s, s_next, u, r, avail_u, avail_u_next, terminated = batch['s'], batch['s_next'], batch['u'], \
batch['r'], batch['avail_u'], batch['avail_u_next'], \
batch['terminated']
mask = 1 - batch["padded"].float() # 用来把那些填充的经验的TD-error置0，从而不让它们影响到学习
# 得到每个agent对应的Q值，维度为(episode个数, max_episode_len， n_agents， n_actions)
q_evals, q_targets = self.get_q_values(batch, max_episode_len)
if self.args.cuda:
s = s.cuda()
u = u.cuda()
r = r.cuda()
s_next = s_next.cuda()
terminated = terminated.cuda()
mask = mask.cuda()
# 取每个agent动作对应的Q值，并且把最后不需要的一维去掉，因为最后一维只有一个值了
q_evals = torch.gather(q_evals, dim=3, index=u).squeeze(3)
# 得到target_q
q_targets[avail_u_next == 0.0] = - 9999999
q_targets = q_targets.max(dim=3)[0]
q_total_eval = self.eval_qmix_net(q_evals, s)
q_total_target = self.target_qmix_net(q_targets, s_next)
targets = r + self.args.gamma * q_total_target * (1 - terminated)
td_error = (q_total_eval - targets.detach())
masked_td_error = mask * td_error # 抹掉填充的经验的td_error
# 不能直接用mean，因为还有许多经验是没用的，所以要求和再比真实的经验数，才是真正的均值
loss = (masked_td_error ** 2).sum() / mask.sum()
self.optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(self.eval_parameters, self.args.grad_norm_clip)
self.optimizer.step()
if train_step > 0 and train_step % self.args.target_update_cycle == 0:
self.target_rnn.load_state_dict(self.eval_rnn.state_dict())
self.target_qmix_net.load_state_dict(self.eval_qmix_net.state_dict())
def _get_inputs(self, batch, transition_idx):
# 取出所有episode上该transition_idx的经验，u_onehot要取出所有，因为要用到上一条
obs, obs_next, u_onehot = batch['o'][:, transition_idx], \
batch['o_next'][:, transition_idx], batch['u_onehot'][:]
episode_num = obs.shape[0]
inputs, inputs_next = [], []
inputs.append(obs)
inputs_next.append(obs_next)
# 给obs添加上一个动作、agent编号
if self.args.last_action:
if transition_idx == 0: # 如果是第一条经验，就让前一个动作为0向量
inputs.append(torch.zeros_like(u_onehot[:, transition_idx]))
else:
inputs.append(u_onehot[:, transition_idx - 1])
inputs_next.append(u_onehot[:, transition_idx])
if self.args.reuse_network:
# 因为当前的obs三维的数据，每一维分别代表(episode编号，agent编号，obs维度)，直接在dim_1上添加对应的向量
# 即可，比如给agent_0后面加(1, 0, 0, 0, 0)，表示5个agent中的0号。而agent_0的数据正好在第0行，那么需要加的
# agent编号恰好就是一个单位矩阵，即对角线为1，其余为0
inputs.append(torch.eye(self.args.n_agents).unsqueeze(0).expand(episode_num, -1, -1))
inputs_next.append(torch.eye(self.args.n_agents).unsqueeze(0).expand(episode_num, -1, -1))
# 要把obs中的三个拼起来，并且要把episode_num个episode、self.args.n_agents个agent的数据拼成40条(40,96)的数据，
# 因为这里所有agent共享一个神经网络，每条数据中带上了自己的编号，所以还是自己的数据
inputs = torch.cat([x.reshape(episode_num * self.args.n_agents, -1) for x in inputs], dim=1)
inputs_next = torch.cat([x.reshape(episode_num * self.args.n_agents, -1) for x in inputs_next], dim=1)
return inputs, inputs_next
def get_q_values(self, batch, max_episode_len):
episode_num = batch['o'].shape[0]
q_evals, q_targets = [], []
for transition_idx in range(max_episode_len):
inputs, inputs_next = self._get_inputs(batch, transition_idx) # 给obs加last_action、agent_id
if self.args.cuda:
inputs = inputs.cuda()
inputs_next = inputs_next.cuda()
self.eval_hidden = self.eval_hidden.cuda()
self.target_hidden = self.target_hidden.cuda()
# q_eval, self.eval_hidden = self.eval_rnn(inputs, self.eval_hidden)
# q_target, self.target_hidden = self.target_rnn(inputs_next, self.target_hidden)
q_eval = self.eval_rnn(inputs)
q_target = self.target_rnn(inputs_next)
# 把q_eval维度重新变回(8, 5,n_actions)
q_eval = q_eval.view(episode_num, self.n_agents, -1)
q_target = q_target.view(episode_num, self.n_agents, -1)
q_evals.append(q_eval)
q_targets.append(q_target)
# 得的q_eval和q_target是一个列表，列表里装着max_episode_len个数组，数组的的维度是(episode个数, n_agents，n_actions)
# 把该列表转化成(episode个数, max_episode_len， n_agents，n_actions)的数组
q_evals = torch.stack(q_evals, dim=1)
q_targets = torch.stack(q_targets, dim=1)
return q_evals, q_targets
def init_hidden(self, episode_num):
# 为每个episode中的每个agent都初始化一个eval_hidden、target_hidden
self.eval_hidden = torch.zeros((episode_num, self.n_agents, self.args.rnn_hidden_dim))
self.target_hidden = torch.zeros((episode_num, self.n_agents, self.args.rnn_hidden_dim))
def save_model(self, train_step):
num = str(train_step // self.args.save_cycle)
if not os.path.exists(self.model_dir):
os.makedirs(self.model_dir)
torch.save(self.eval_qmix_net.state_dict(), self.model_dir + '/' + num + '_qmix_net_params.pkl')
torch.save(self.eval_rnn.state_dict(), self.model_dir + '/' + num + '_rnn_net_params.pkl')
class MAVEN:
def __init__(self, args):
self.n_actions = args.n_actions
self.n_agents = args.n_agents
self.state_shape = args.state_shape
self.obs_shape = args.obs_shape
# input shaoe of rnn
input_shape = self.obs_shape
if args.last_action:
input_shape += self.n_actions
if args.reuse_network:
input_shape += self.n_agents
# network
self.z_policy = HierarchicalPolicy(args) # choose z
self.eval_rnn = BootstrappedRNN(input_shape,
args) # choose action for each agent
self.target_rnn = BootstrappedRNN(input_shape, args)
self.eval_qmix_net = QMixNet(args) # mix the q value
self.target_qmix_net = QMixNet(args)
self.mi_net = VarDistribution(args) # get q(z|sigma(tau))
self.args = args
if self.args.cuda:
self.z_policy.cuda()
self.eval_rnn.cuda()
self.target_rnn.cuda()
self.eval_qmix_net.cuda()
self.target_qmix_net.cuda()
self.mi_net.cuda()
self.model_dir = args.model_dir + '/' + args.alg + '/' + args.env_name + '/' + str(
args.map_size)
# 如果存在模型则加载模型
if self.args.load_model:
if os.path.exists(self.model_dir + '/rnn_net_params.pkl'):
path_z_policy = self.model_dir + '/z_policy_params.pkl'
path_rnn = self.model_dir + '/rnn_net_params.pkl'
path_qmix = self.model_dir + '/qmix_net_params.pkl'
path_mi = self.model_dir + '/mi_net_params.pkl'
map_location = 'cuda:0' if self.args.cuda else 'cpu'
self.z_policy.load_state_dict(
torch.load(path_z_policy, map_location=map_location))
self.eval_rnn.load_state_dict(
torch.load(path_rnn, map_location=map_location))
# self.eval_qmix_net.load_state_dict(torch.load(path_qmix, map_location=map_location))
# self.mi_net.load_state_dict(torch.load(path_mi, map_location=map_location))
print('Successfully load the model: {}, {}, {} and {}'.format(
path_z_policy, path_rnn, path_qmix, path_mi))
else:
raise Exception("No model!")
# 让target_net和eval_net的网络参数相同
self.target_rnn.load_state_dict(self.eval_rnn.state_dict())
self.target_qmix_net.load_state_dict(self.eval_qmix_net.state_dict())
self.eval_parameters = list(self.z_policy.parameters()) + list(self.eval_qmix_net.parameters()) + \
list(self.eval_rnn.parameters()) + list(self.mi_net.parameters())
if args.optimizer == "RMS":
self.optimizer = torch.optim.RMSprop(self.eval_parameters,
lr=args.lr)
# 执行过程中，要为每个agent都维护一个eval_hidden
# 学习过程中，要为每个episode的每个agent都维护一个eval_hidden、target_hidden
self.eval_hidden = None
self.target_hidden = None
print('Init alg MAVEN')
def learn(self,
batch,
max_episode_len,
train_step,
epsilon=None): # train_step表示是第几次学习，用来控制更新target_net网络的参数
'''
在learn的时候，抽取到的数据是四维的，四个维度分别为 1——第几个episode 2——episode中第几个transition
3——第几个agent的数据 4——具体obs维度。因为在选动作时不仅需要输入当前的inputs，还要给神经网络输入hidden_state，
hidden_state和之前的经验相关，因此就不能随机抽取经验进行学习。所以这里一次抽取多个episode，然后一次给神经网络
传入每个episode的同一个位置的transition
'''
episode_num = batch['o'].shape[0]
self.init_hidden(episode_num)
for key in batch.keys(): # 把batch里的数据转化成tensor
if key == 'u':
batch[key] = torch.tensor(batch[key], dtype=torch.long)
else:
batch[key] = torch.tensor(batch[key], dtype=torch.float32)
s, s_next, u, r, avail_u, avail_u_next, terminated, z = batch['s'], batch['s_next'], batch['u'], batch['r'], \
batch['avail_u'], batch['avail_u_next'], \
batch['terminated'], batch['z']
mask = 1 - batch["padded"].float() # 用来把那些填充的经验的TD-error置0，从而不让它们影响到学习
# 得到每个agent对应的Q值，维度为(episode个数, max_episode_len， n_agents， n_actions)
q_evals, q_targets = self.get_q_values(batch, max_episode_len)
if self.args.cuda:
s = s.cuda()
u = u.cuda()
r = r.cuda()
avail_u = avail_u.cuda()
s_next = s_next.cuda()
terminated = terminated.cuda()
mask = mask.cuda()
z = z.cuda()
# -------------------------------------------------RL Loss------------------------------------------------------
z_prob = self.z_policy(s[:, 0, :])
log_z_prob = torch.log(z_prob)
entropy = -(z_prob * log_z_prob).sum(dim=-1).detach()
class_z = z.long().argmax(
dim=-1, keepdim=True) # transform onehot vector to int
z_prob_taken = torch.gather(z_prob, index=class_z, dim=-1).squeeze(-1)
z_returns = r.sum(dim=1)
rl_loss = -(
z_prob_taken *
(z_returns + self.args.entropy_coefficient * entropy)).mean()
# -------------------------------------------------RL Loss------------------------------------------------------
# -------------------------------------------------MI Loss------------------------------------------------------
# get length for each episode
episode_length = []
for episode_idx in range(episode_num):
for transition_idx in range(self.args.episode_limit):
if terminated[episode_idx, transition_idx, 0] == 1:
episode_length.append(transition_idx + 1)
break
episode_len = torch.tensor(episode_length, dtype=torch.long)
mi_prob = self.mi_net(q_evals.clone(), avail_u, s,
episode_len) # (episode_num, args.noise_dim)
class_z = class_z.squeeze(-1)
mi_loss = f.cross_entropy(mi_prob, class_z)
# -------------------------------------------------MI Loss------------------------------------------------------
# -------------------------------------------------QL Loss------------------------------------------------------
# 取每个agent动作对应的Q值，并且把最后不需要的一维去掉，因为最后一维只有一个值了
q_evals = torch.gather(q_evals, dim=3, index=u).squeeze(3)
# 得到target_q
q_targets[avail_u_next == 0.0] = -9999999
q_targets = q_targets.max(dim=3)[0]
q_total_eval = self.eval_qmix_net(q_evals, s)
q_total_target = self.target_qmix_net(q_targets, s_next)
targets = r + self.args.gamma * q_total_target * (1 - terminated)
td_error = (q_total_eval - targets.detach())
masked_td_error = mask * td_error # 抹掉填充的经验的td_error
ql_loss = (masked_td_error**2).sum() / mask.sum()
# -------------------------------------------------QL Loss------------------------------------------------------
loss = rl_loss + self.args.lambda_mi * mi_loss + self.args.lambda_ql * ql_loss
self.optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(self.eval_parameters,
self.args.grad_norm_clip)
self.optimizer.step()
# print('mi_loss is {}, ql_loss is {}'.format(mi_loss, ql_loss))
# print('Training params:')
# for params in self.eval_rnn.named_parameters():
# print(params)
if train_step > 0 and train_step % self.args.target_update_cycle == 0:
self.target_rnn.load_state_dict(self.eval_rnn.state_dict())
self.target_qmix_net.load_state_dict(
self.eval_qmix_net.state_dict())
def _get_inputs(self, batch, transition_idx):
# 取出所有episode上该transition_idx的经验，u_onehot要取出所有，因为要用到上一条
obs, obs_next, u_onehot = batch['o'][:, transition_idx], \
batch['o_next'][:, transition_idx], batch['u_onehot'][:]
episode_num = obs.shape[0]
inputs, inputs_next = [], []
inputs.append(obs)
inputs_next.append(obs_next)
# 给obs添加上一个动作、agent编号
if self.args.last_action:
if transition_idx == 0: # 如果是第一条经验，就让前一个动作为0向量
inputs.append(torch.zeros_like(u_onehot[:, transition_idx]))
else:
inputs.append(u_onehot[:, transition_idx - 1])
inputs_next.append(u_onehot[:, transition_idx])
if self.args.reuse_network:
# 因为当前的obs三维的数据，每一维分别代表(episode编号，agent编号，obs维度)，直接在dim_1上添加对应的向量
# 即可，比如给agent_0后面加(1, 0, 0, 0, 0)，表示5个agent中的0号。而agent_0的数据正好在第0行，那么需要加的
# agent编号恰好就是一个单位矩阵，即对角线为1，其余为0
inputs.append(
torch.eye(self.args.n_agents).unsqueeze(0).expand(
episode_num, -1, -1))
inputs_next.append(
torch.eye(self.args.n_agents).unsqueeze(0).expand(
episode_num, -1, -1))
# 要把obs中的三个拼起来，并且要把episode_num个episode、self.args.n_agents个agent的数据拼成40条(40,96)的数据，
# 因为这里所有agent共享一个神经网络，每条数据中带上了自己的编号，所以还是自己的数据
inputs = torch.cat(
[x.reshape(episode_num * self.args.n_agents, -1) for x in inputs],
dim=1)
inputs_next = torch.cat([
x.reshape(episode_num * self.args.n_agents, -1)
for x in inputs_next
],
dim=1)
return inputs, inputs_next
def get_q_values(self, batch, max_episode_len):
episode_num = batch['o'].shape[0]
z = batch['z'].repeat(self.n_agents, 1) # each episode has one z
q_evals, q_targets = [], []
for transition_idx in range(max_episode_len):
inputs, inputs_next = self._get_inputs(
batch, transition_idx) # add last_action、agent_id
if self.args.cuda:
z = z.cuda()
inputs = inputs.cuda()
inputs_next = inputs_next.cuda()
self.eval_hidden = self.eval_hidden.cuda()
self.target_hidden = self.target_hidden.cuda()
q_eval, self.eval_hidden = self.eval_rnn(inputs, self.eval_hidden,
z)
q_target, self.target_hidden = self.target_rnn(
inputs_next, self.target_hidden, z)
# 把q_eval维度重新变回(8, 5,n_actions)
q_eval = q_eval.view(episode_num, self.n_agents, -1)
q_target = q_target.view(episode_num, self.n_agents, -1)
q_evals.append(q_eval)
q_targets.append(q_target)
# 得的q_eval和q_target是一个列表，列表里装着max_episode_len个数组，数组的的维度是(episode个数, n_agents，n_actions)
# 把该列表转化成(episode个数, max_episode_len， n_agents，n_actions)的数组
q_evals = torch.stack(q_evals, dim=1)
q_targets = torch.stack(q_targets, dim=1)
return q_evals, q_targets
def init_hidden(self, episode_num):
# 为每个episode中的每个agent都初始化一个eval_hidden、target_hidden
if self.args.load_model:
self.eval_hidden = torch.zeros(
(episode_num, int(self.n_agents * self.args.est_mod_num),
self.args.rnn_hidden_dim))
self.target_hidden = torch.zeros(
(episode_num, int(self.n_agents * self.args.est_mod_num),
self.args.rnn_hidden_dim))
else:
self.eval_hidden = torch.zeros(
(episode_num, self.n_agents, self.args.rnn_hidden_dim))
self.target_hidden = torch.zeros(
(episode_num, self.n_agents, self.args.rnn_hidden_dim))
def save_model(self, train_step):
num = str(train_step // self.args.save_cycle)
if not os.path.exists(self.model_dir):
os.makedirs(self.model_dir)
torch.save(self.z_policy.state_dict(),
self.model_dir + '/' + num + '_z_policy_params.pkl')
torch.save(self.mi_net.state_dict(),
self.model_dir + '/' + num + '_mi_net_params.pkl')
torch.save(self.eval_qmix_net.state_dict(),
self.model_dir + '/' + num + '_qmix_net_params.pkl')
torch.save(self.eval_rnn.state_dict(),
self.model_dir + '/' + num + '_rnn_net_params.pkl')
class QMIX:
def __init__(self, args):
self.n_actions = args.n_actions
self.n_agents = args.n_agents
self.state_shape = args.state_shape
self.obs_shape = args.obs_shape
input_shape = self.obs_shape
# 根据参数决定RNN的输入维度
if args.last_action:
input_shape += self.n_actions
if args.reuse_network:
input_shape += self.n_agents
# 神经网络
self.eval_rnn = RNN(input_shape, args) # 每个agent选动作的网络
self.target_rnn = RNN(input_shape, args)
self.eval_qmix_net = QMixNet(args) # 把agentsQ值加起来的网络
self.target_qmix_net = QMixNet(args)
# 如果存在模型则加载模型
if args.model_dir != '':
path_rnn = args.model_dir + '/rnn_net_params.pkl'
path_qmix = args.model_dir + '/qmix_net_params.pkl'
self.eval_rnn.load_state_dict(
torch.load(args.model_dir + '/rnn_net_params.pkl'))
self.eval_qmix_net.load_state_dict(
torch.load(args.model_dir + '/qmix_net_params.pkl'))
print('Successfully load the rnn model {} and the qmix model {}'.
format(path_rnn, path_qmix))
# 让target_net和eval_net的网络参数相同
self.target_rnn.load_state_dict(self.eval_rnn.state_dict())
self.target_qmix_net.load_state_dict(self.eval_qmix_net.state_dict())
self.eval_parameters = list(self.eval_qmix_net.parameters()) + list(
self.eval_rnn.parameters())
if args.optimizer == "RMS":
self.optimizer = torch.optim.RMSprop(self.eval_parameters,
lr=args.lr)
self.args = args
# 执行过程中，要为每个agent都维护一个eval_hidden
# 学习过程中，要为每个episode的每个agent都维护一个eval_hidden、target_hidden
self.eval_hidden = None
self.target_hidden = None
def learn(self, batch, max_episode_len,
train_step): # train_step表示是第几次学习，用来控制更新target_net网络的参数
'''
在learn的时候，抽取到的数据是四维的，四个维度分别为 1——第几个episode 2——episode中第几个transition
3——第几个agent的数据 4——具体obs维度。因为在选动作时不仅需要输入当前的inputs，还要给神经网络输入hidden_state，
hidden_state和之前的经验相关，因此就不能随机抽取经验进行学习。所以这里一次抽取多个episode，然后一次给神经网络
传入每个episode的同一个位置的transition
'''
episode_num = batch['o'].shape[0]
self.init_hidden(episode_num)
for key in batch.keys(): # 把batch里的数据转化成tensor
if key == 'u':
batch[key] = torch.tensor(batch[key], dtype=torch.long)
else:
batch[key] = torch.tensor(batch[key], dtype=torch.float32)
s, s_next, u, r, avail_u, terminated = batch['s'], batch['s_next'], batch['u'], \
batch['r'], batch['avail_u'], batch['terminated']
avail_u_next = avail_u[:, 1:] # 下一个obs可以执行的动作，要用来计算target_q
mask = 1 - batch["padded"].float() # 用来把那些填充的经验的TD-error置0，从而不让它们影响到学习
# 得到每个agent对应的Q值，维度为(episode个数, max_episode_len， n_agents， n_actions)
q_evals, q_targets = self.get_q_values(batch, max_episode_len)
# 取每个agent动作对应的Q值，并且把最后不需要的一维去掉，因为最后一维只有一个值了
q_evals = torch.gather(q_evals, dim=3, index=u).squeeze(3)
# 得到真正的target_q
# 传入的avail_u_next参数和q_targets维度一样，这样每个都对应起来，只要为0，就证明在该episode中，该transition上该agent不能执行该动作
q_targets[avail_u_next == 0.0] = -9999999
q_targets = q_targets.max(dim=3)[0]
q_total_eval = self.eval_qmix_net.forward(q_evals, s)
q_total_target = self.target_qmix_net.forward(q_targets, s_next)
targets = r + self.args.gamma * q_total_target * (1 - terminated)
td_error = (q_total_eval - targets.detach()) # TODO 还差一个括号，有没有影响
masked_td_error = mask * td_error # 抹掉填充的经验的td_error
# loss = masked_td_error.pow(2).mean()
# 不能直接用mean，因为还有许多经验是没用的，所以要求和再比真实的经验数，才是真正的均值
loss = (masked_td_error**2).sum() / mask.sum()
# a = self.eval_qmix_net.named_parameters()
# for i in a:
# print(i)
# print('train_step is {}'.format(train_step))
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
if train_step > 0 and train_step % self.args.target_update_cycle == 0:
self.target_rnn.load_state_dict(self.eval_rnn.state_dict())
self.target_qmix_net.load_state_dict(
self.eval_qmix_net.state_dict())
def _get_inputs(self, batch, transition_idx):
# 取出所有episode上该transition_idx的经验，u_onehot要取出所有，因为要用到上一条
obs, obs_next, u_onehot = batch['o'][:, transition_idx], \
batch['o_next'][:, transition_idx], batch['u_onehot'][:]
episode_num = obs.shape[0]
inputs, inputs_next = [], []
inputs.append(obs)
inputs_next.append(obs_next)
# 给obs添加上一个动作、agent编号
if self.args.last_action:
if transition_idx == 0: # 如果是第一条经验，就让前一个动作为0向量
inputs.append(torch.zeros_like(u_onehot[:, transition_idx]))
else:
inputs.append(u_onehot[:, transition_idx - 1])
inputs_next.append(u_onehot[:, transition_idx])
if self.args.reuse_network:
# 因为当前的obs三维的数据，每一维分别代表(episode编号，agent编号，obs维度)，直接在dim_1上添加对应的向量
# 即可，比如给agent_0后面加(1, 0, 0, 0, 0)，表示5个agent中的0号。而agent_0的数据正好在第0行，那么需要加的
# agent编号恰好就是一个单位矩阵，即对角线为1，其余为0
inputs.append(
torch.eye(self.args.n_agents).unsqueeze(0).expand(
episode_num, -1, -1))
inputs_next.append(
torch.eye(self.args.n_agents).unsqueeze(0).expand(
episode_num, -1, -1))
# 要把obs中的三个拼起来，并且要把episode_num个episode、self.args.n_agents个agent的数据拼成40条(40,96)的数据，
# 因为这里所有agent共享一个神经网络，每条数据中带上了自己的编号，所以还是自己的数据
inputs = torch.cat(
[x.reshape(episode_num * self.args.n_agents, -1) for x in inputs],
dim=1)
inputs_next = torch.cat([
x.reshape(episode_num * self.args.n_agents, -1)
for x in inputs_next
],
dim=1)
# TODO 检查inputs_next是不是相当于inputs向后移动一条
return inputs, inputs_next
def get_q_values(self, batch, max_episode_len):
episode_num = batch['o'].shape[0]
q_evals, q_targets = [], []
for transition_idx in range(max_episode_len):
inputs, inputs_next = self._get_inputs(
batch, transition_idx) # 给obs加last_action、agent_id
q_eval, self.eval_hidden = self.eval_rnn.forward(
inputs, self.eval_hidden
) # inputs维度为(40,96)，得到的q_eval维度为(40,n_actions)
q_target, self.target_hidden = self.target_rnn.forward(
inputs_next, self.target_hidden)
# 把q_eval维度重新变回(8, 5,n_actions)
q_eval = q_eval.view(episode_num, self.n_agents, -1)
q_target = q_target.view(episode_num, self.n_agents, -1)
q_evals.append(q_eval)
q_targets.append(q_target)
# 得的q_eval和q_target是一个列表，列表里装着max_episode_len个数组，数组的的维度是(episode个数, n_agents，n_actions)
# 把该列表转化成(episode个数, max_episode_len， n_agents，n_actions)的数组
q_evals = torch.stack(q_evals, dim=1)
q_targets = torch.stack(q_targets, dim=1)
return q_evals, q_targets
def init_hidden(self, episode_num):
# 为每个episode中的每个agent都初始化一个eval_hidden、target_hidden
self.eval_hidden = self.eval_rnn.init_hidden().unsqueeze(0).expand(
episode_num, self.n_agents, -1)
self.target_hidden = self.target_rnn.init_hidden().unsqueeze(0).expand(
episode_num, self.n_agents, -1)
def save_model(self, train_step):
num = str(train_step // self.args.save_cycle)
torch.save(self.eval_qmix_net.state_dict(),
self.args.result_dir + '/' + num + '_qmix_net_params.pkl')
torch.save(self.eval_rnn.state_dict(),
self.args.result_dir + '/' + num + '_rnn_net_params.pkl')