class QtranAlt:
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
rnn_input_shape = self.obs_shape
# 根据参数决定RNN的输入维度
if args.last_action:
rnn_input_shape += self.n_actions # 当前agent的上一个动作的one_hot向量
if args.reuse_network:
rnn_input_shape += self.n_agents
self.args = args
# 神经网络
self.eval_rnn = RNN(rnn_input_shape, args) # individual networks
self.target_rnn = RNN(rnn_input_shape, args)
self.eval_joint_q = QtranQAlt(args) # counterfactual joint networks
self.target_joint_q = QtranQAlt(args)
self.v = QtranV(args)
if self.args.cuda:
self.eval_rnn.cuda()
self.target_rnn.cuda()
self.eval_joint_q.cuda()
self.target_joint_q.cuda()
self.v.cuda()
self.model_dir = args.model_dir + '/' + args.alg + '/' + args.map
# 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_joint_q = self.model_dir + '/joint_q_params.pkl'
path_v = self.model_dir + '/v_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_joint_q.load_state_dict(
torch.load(path_joint_q, map_location=map_location))
self.v.load_state_dict(
torch.load(path_v, map_location=map_location))
print('Successfully load the model: {}, {} and {}'.format(
path_rnn, path_joint_q, path_v))
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_joint_q.load_state_dict(self.eval_joint_q.state_dict())
self.eval_parameters = list(self.eval_joint_q.parameters()) + \
list(self.v.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 QTRAN-alt')
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().repeat(
1, 1, self.n_agents) # 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
if self.args.cuda:
u = u.cuda()
r = r.cuda()
avail_u = avail_u.cuda()
avail_u_next = avail_u_next.cuda()
terminated = terminated.cuda()
mask = mask.cuda()
# 得到每个agent对应的Q和hidden_states，维度为(episode个数, max_episode_len， n_agents， n_actions/hidden_dim)
individual_q_evals, individual_q_targets, hidden_evals, hidden_targets = self._get_individual_q(
batch, max_episode_len)
# 得到当前时刻和下一时刻每个agent的局部最优动作及其one_hot表示
individual_q_clone = individual_q_evals.clone()
individual_q_clone[avail_u == 0.0] = -999999
individual_q_targets[avail_u_next == 0.0] = -999999
opt_onehot_eval = torch.zeros(*individual_q_clone.shape)
opt_action_eval = individual_q_clone.argmax(dim=3, keepdim=True)
opt_onehot_eval = opt_onehot_eval.scatter(-1,
opt_action_eval[:, :].cpu(),
1)
opt_onehot_target = torch.zeros(*individual_q_targets.shape)
opt_action_target = individual_q_targets.argmax(dim=3, keepdim=True)
opt_onehot_target = opt_onehot_target.scatter(
-1, opt_action_target[:, :].cpu(), 1)
# ---------------------------------------------L_td-------------------------------------------------------------
# 计算joint_q和v，要注意joint_q是每个agent都有，v只有一个
# joint_q的维度为(episode个数, max_episode_len， n_agents， n_actions), 而且joint_q在后面的l_nopt还要用到
# v的维度为(episode个数, max_episode_len)
joint_q_evals, joint_q_targets, v = self.get_qtran(
batch, opt_onehot_target, hidden_evals, hidden_targets)
# 取出当前agent动作对应的joint_q_chosen以及它的局部最优动作对应的joint_q
joint_q_chosen = torch.gather(joint_q_evals, dim=-1, index=u).squeeze(
-1) # (episode个数, max_episode_len, n_agents)
joint_q_opt = torch.gather(joint_q_targets,
dim=-1,
index=opt_action_target).squeeze(-1)
# loss
y_dqn = r.repeat(1, 1,
self.n_agents) + self.args.gamma * joint_q_opt * (
1 - terminated.repeat(1, 1, self.n_agents))
td_error = joint_q_chosen - y_dqn.detach()
l_td = ((td_error * mask)**2).sum() / mask.sum()
# ---------------------------------------------L_td-------------------------------------------------------------
# ---------------------------------------------L_opt------------------------------------------------------------
# 将局部最优动作的Q值相加 (episode个数,max_episode_len)
# 这里要使用individual_q_clone，它把不能执行的动作Q值改变了，使用individual_q_evals可能会使用不能执行的动作的Q值
q_sum_opt = individual_q_clone.max(dim=-1)[0].sum(dim=-1)
# 重新得到joint_q_opt_eval，它和joint_q_evals的区别是前者输入的动作是当前局部最优动作，后者输入的动作是当前执行的动作
joint_q_opt_evals, _, _ = self.get_qtran(batch,
opt_onehot_eval,
hidden_evals,
hidden_targets,
hat=True)
joint_q_opt_evals = torch.gather(
joint_q_opt_evals, dim=-1, index=opt_action_eval).squeeze(
-1) # (episode个数, max_episode_len， n_agents)
# 因为QTRAN-alt要对每个agent都计算l_opt，所以要把q_sum_opt和v再增加一个agent维
q_sum_opt = q_sum_opt.unsqueeze(-1).expand(-1, -1, self.n_agents)
v = v.unsqueeze(-1).expand(-1, -1, self.n_agents)
opt_error = q_sum_opt - joint_q_opt_evals.detach(
) + v # 计算l_opt时需要将joint_q_opt_evals固定
l_opt = ((opt_error * mask)**2).sum() / mask.sum()
# ---------------------------------------------L_opt------------------------------------------------------------
# ---------------------------------------------L_nopt-----------------------------------------------------------
# 因为L_nopt约束的是当前agent所有可执行的动作中，对应的最小的d，为了让不能执行的动作不影响d的计算，将不能执行的动作对应的q变大
individual_q_evals[avail_u == 0.0] = 999999
# 得到agent_i之外的其他agent的执行动作的Q值之和q_other_sum
# 1. 先得到每个agent的执行动作的Q值q_all,(episode个数, max_episode_len, n_agents， 1)
q_all_chosen = torch.gather(individual_q_evals, dim=-1, index=u)
# 2. 把q_all最后一个维度上当前agent的Q值变成所有agent的Q值，(episode个数, max_episode_len, n_agents, n_agents)
q_all_chosen = q_all_chosen.view((episode_num, max_episode_len, 1,
-1)).repeat(1, 1, self.n_agents, 1)
q_mask = (1 - torch.eye(self.n_agents)).unsqueeze(0).unsqueeze(0)
if self.args.cuda:
q_mask = q_mask.cuda()
q_other_chosen = q_all_chosen * q_mask # 把每个agent自己的Q值置为0，从而才能相加得到其他agent的Q值之和
# 3. 求和，同时由于对于当前agent的每个动作，都要和q_other_sum相加，所以把q_other_sum扩展出n_actions维度
q_other_sum = q_other_chosen.sum(dim=-1, keepdim=True).repeat(
1, 1, 1, self.n_actions)
# 当前agent的每个动作的Q和其他agent执行动作的Q相加，得到D中的第一项
q_sum_nopt = individual_q_evals + q_other_sum
# 因为joint_q_evals的维度是(episode个数,max_episode_len,n_agents,n_actions)，所以要对v扩展出一个n_actions维度
v = v.unsqueeze(-1).expand(-1, -1, -1, self.n_actions)
d = q_sum_nopt - joint_q_evals.detach(
) + v # 计算l_nopt时需要将qtran_q_evals固定
d = d.min(dim=-1)[0]
l_nopt = ((d * mask)**2).sum() / mask.sum()
# ---------------------------------------------L_nopt-----------------------------------------------------------
# print('l_td is {}, l_opt is {}, l_nopt is {}'.format(l_td, l_opt, l_nopt))
loss = l_td + self.args.lambda_opt * l_opt + self.args.lambda_nopt * l_nopt
# loss = l_td + self.args.lambda_opt * l_opt
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_joint_q.load_state_dict(self.eval_joint_q.state_dict())
def _get_individual_q(self, batch, max_episode_len):
episode_num = batch['o'].shape[0]
q_evals, q_targets, hidden_evals, hidden_targets = [], [], [], []
for transition_idx in range(max_episode_len):
inputs, inputs_next = self._get_individual_inputs(
batch, transition_idx) # 给obs加last_action、agent_id
if self.args.cuda:
inputs = inputs.cuda()
self.eval_hidden = self.eval_hidden.cuda()
inputs_next = inputs_next.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)
hidden_eval, hidden_target = self.eval_hidden.clone(
), self.target_hidden.clone()
# 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)
hidden_eval = hidden_eval.view(episode_num, self.n_agents, -1)
hidden_target = hidden_target.view(episode_num, self.n_agents, -1)
q_evals.append(q_eval)
q_targets.append(q_target)
hidden_evals.append(hidden_eval)
hidden_targets.append(hidden_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)
hidden_evals = torch.stack(hidden_evals, dim=1)
hidden_targets = torch.stack(hidden_targets, dim=1)
return q_evals, q_targets, hidden_evals, hidden_targets
def _get_individual_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_qtran(self,
batch,
local_opt_actions,
hidden_evals,
hidden_targets=None,
hat=False):
episode_num, max_episode_len, _, _ = hidden_evals.shape
s = batch['s'][:, :max_episode_len]
s_next = batch['s_next'][:, :max_episode_len]
u_onehot = batch['u_onehot'][:, :max_episode_len]
v_state = s.clone()
# s和s_next没有n_agents维度，每个agent的joint_q网络都需要, 所以要把s转化成四维
s = s.unsqueeze(-2).expand(-1, -1, self.n_agents, -1)
s_next = s_next.unsqueeze(-2).expand(-1, -1, self.n_agents, -1)
# 添加agent编号对应的one-hot向量
'''
因为当前的inputs三维的数据，每一维分别代表(episode编号，agent编号，inputs维度)，直接在后面添加对应的向量
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
'''
action_onehot = torch.eye(
self.n_agents).unsqueeze(0).unsqueeze(0).expand(
episode_num, max_episode_len, -1, -1)
s_eval = torch.cat([s, action_onehot], dim=-1)
s_target = torch.cat([s_next, action_onehot], dim=-1)
if self.args.cuda:
s_eval = s_eval.cuda()
s_target = s_target.cuda()
v_state = v_state.cuda()
u_onehot = u_onehot.cuda()
hidden_evals = hidden_evals.cuda()
hidden_targets = hidden_targets.cuda()
local_opt_actions = local_opt_actions.cuda()
if hat:
# 神经网络输出的q_eval、q_target的维度为(episode_num * max_episode_len * n_agents, n_actions)
q_evals = self.eval_joint_q(s_eval, hidden_evals,
local_opt_actions)
q_targets = None
v = None
# 把q_eval维度变回(episode_num, max_episode_len, n_agents, n_actions)
q_evals = q_evals.view(episode_num, max_episode_len, -1,
self.n_actions)
else:
q_evals = self.eval_joint_q(s_eval, hidden_evals, u_onehot)
q_targets = self.target_joint_q(s_target, hidden_targets,
local_opt_actions)
v = self.v(v_state, hidden_evals)
# 把q_eval、q_target维度变回(episode_num, max_episode_len, n_agents, n_actions)
q_evals = q_evals.view(episode_num, max_episode_len, -1,
self.n_actions)
q_targets = q_targets.view(episode_num, max_episode_len, -1,
self.n_actions)
# 把v维度变回(episode_num, max_episode_len)
v = v.view(episode_num, -1)
return q_evals, q_targets, v
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_rnn.state_dict(),
self.model_dir + '/' + num + '_rnn_net_params.pkl')
torch.save(self.eval_joint_q.state_dict(),
self.model_dir + '/' + num + '_joint_q_params.pkl')
torch.save(self.v.state_dict(),
self.model_dir + '/' + num + '_v_params.pkl')
class QtranBase:
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
self.args = args
rnn_input_shape = self.obs_shape
# 根据参数决定RNN的输入维度
if args.last_action:
rnn_input_shape += self.n_actions # 当前agent的上一个动作的one_hot向量
if args.reuse_network:
rnn_input_shape += self.n_agents
# 神经网络
self.eval_rnn = RNN(rnn_input_shape, args) # 每个agent选动作的网络
self.target_rnn = RNN(rnn_input_shape, args)
self.eval_joint_q = QtranQBase(args) # Joint action-value network
self.target_joint_q = QtranQBase(args)
self.v = QtranV(args)
if self.args.cuda:
self.eval_rnn.cuda()
self.target_rnn.cuda()
self.eval_joint_q.cuda()
self.target_joint_q.cuda()
self.v.cuda()
self.model_dir = args.model_dir + '/' + args.alg + '/' + args.map
# 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_joint_q = self.model_dir + '/joint_q_params.pkl'
path_v = self.model_dir + '/v_params.pkl'
self.eval_rnn.load_state_dict(torch.load(path_rnn))
self.eval_joint_q.load_state_dict(torch.load(path_joint_q))
self.v.load_state_dict(torch.load(path_v))
print('Successfully load the model: {}, {} and {}'.format(
path_rnn, path_joint_q, path_v))
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_joint_q.load_state_dict(self.eval_joint_q.state_dict())
self.eval_parameters = list(self.eval_joint_q.parameters()) + \
list(self.v.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 QTRAN-base')
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)
u, r, avail_u, avail_u_next, terminated = batch['u'], batch['r'], batch['avail_u'], \
batch['avail_u_next'], batch['terminated']
mask = (1 - batch["padded"].float()).squeeze(
-1) # 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
if self.args.cuda:
u = u.cuda()
r = r.cuda()
avail_u = avail_u.cuda()
avail_u_next = avail_u_next.cuda()
terminated = terminated.cuda()
mask = mask.cuda()
# 得到每个agent对应的Q和hidden_states，维度为(episode个数, max_episode_len， n_agents， n_actions/hidden_dim)
individual_q_evals, individual_q_targets, hidden_evals, hidden_targets = self._get_individual_q(
batch, max_episode_len)
# 得到当前时刻和下一时刻每个agent的局部最优动作及其one_hot表示
individual_q_clone = individual_q_evals.clone()
individual_q_clone[avail_u == 0.0] = -999999
individual_q_targets[avail_u_next == 0.0] = -999999
opt_onehot_eval = torch.zeros(*individual_q_clone.shape)
opt_action_eval = individual_q_clone.argmax(dim=3, keepdim=True)
opt_onehot_eval = opt_onehot_eval.scatter(-1,
opt_action_eval[:, :].cpu(),
1)
opt_onehot_target = torch.zeros(*individual_q_targets.shape)
opt_action_target = individual_q_targets.argmax(dim=3, keepdim=True)
opt_onehot_target = opt_onehot_target.scatter(
-1, opt_action_target[:, :].cpu(), 1)
# ---------------------------------------------L_td-------------------------------------------------------------
# 计算joint_q和v
# joint_q、v的维度为(episode个数, max_episode_len, 1), 而且joint_q在后面的l_nopt还要用到
joint_q_evals, joint_q_targets, v = self.get_qtran(
batch, hidden_evals, hidden_targets, opt_onehot_target)
# loss
y_dqn = r.squeeze(-1) + self.args.gamma * joint_q_targets * (
1 - terminated.squeeze(-1))
td_error = joint_q_evals - y_dqn.detach()
l_td = ((td_error * mask)**2).sum() / mask.sum()
# ---------------------------------------------L_td-------------------------------------------------------------
# ---------------------------------------------L_opt------------------------------------------------------------
# 将局部最优动作的Q值相加
# 这里要使用individual_q_clone，它把不能执行的动作Q值改变了，使用individual_q_evals可能会使用不能执行的动作的Q值
q_sum_opt = individual_q_clone.max(dim=-1)[0].sum(
dim=-1) # (episode个数, max_episode_len)
# 重新得到joint_q_hat_opt，它和joint_q_evals的区别是前者输入的动作是局部最优动作，后者输入的动作是执行的动作
# (episode个数, max_episode_len)
joint_q_hat_opt, _, _ = self.get_qtran(batch,
hidden_evals,
hidden_targets,
opt_onehot_eval,
hat=True)
opt_error = q_sum_opt - joint_q_hat_opt.detach(
) + v # 计算l_opt时需要将joint_q_hat_opt固定
l_opt = ((opt_error * mask)**2).sum() / mask.sum()
# ---------------------------------------------L_opt------------------------------------------------------------
# ---------------------------------------------L_nopt-----------------------------------------------------------
# 每个agent的执行动作的Q值,(episode个数, max_episode_len, n_agents, 1)
q_individual = torch.gather(individual_q_evals, dim=-1,
index=u).squeeze(-1)
q_sum_nopt = q_individual.sum(dim=-1) # (episode个数, max_episode_len)
nopt_error = q_sum_nopt - joint_q_evals.detach(
) + v # 计算l_nopt时需要将joint_q_evals固定
nopt_error = nopt_error.clamp(max=0)
l_nopt = ((nopt_error * mask)**2).sum() / mask.sum()
# ---------------------------------------------L_nopt-----------------------------------------------------------
# print('l_td is {}, l_opt is {}, l_nopt is {}'.format(l_td, l_opt, l_nopt))
loss = l_td + self.args.lambda_opt * l_opt + self.args.lambda_nopt * l_nopt
# loss = l_td + self.args.lambda_opt * l_opt
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_joint_q.load_state_dict(self.eval_joint_q.state_dict())
def _get_individual_q(self, batch, max_episode_len):
episode_num = batch['o'].shape[0]
q_evals, q_targets, hidden_evals, hidden_targets = [], [], [], []
for transition_idx in range(max_episode_len):
inputs, inputs_next = self._get_individual_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()
# 要用第一条经验把target网络的hidden_state初始化好，直接用第二条经验传入target网络不对
if transition_idx == 0:
_, self.target_hidden = self.target_rnn(
inputs, self.eval_hidden)
q_eval, self.eval_hidden = self.eval_rnn(
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(
inputs_next, self.target_hidden)
hidden_eval, hidden_target = self.eval_hidden.clone(
), self.target_hidden.clone()
# 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)
hidden_eval = hidden_eval.view(episode_num, self.n_agents, -1)
hidden_target = hidden_target.view(episode_num, self.n_agents, -1)
q_evals.append(q_eval)
q_targets.append(q_target)
hidden_evals.append(hidden_eval)
hidden_targets.append(hidden_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)
hidden_evals = torch.stack(hidden_evals, dim=1)
hidden_targets = torch.stack(hidden_targets, dim=1)
return q_evals, q_targets, hidden_evals, hidden_targets
def _get_individual_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_qtran(self,
batch,
hidden_evals,
hidden_targets,
local_opt_actions,
hat=False):
episode_num, max_episode_len, _, _ = hidden_targets.shape
states = batch['s'][:, :max_episode_len]
states_next = batch['s_next'][:, :max_episode_len]
u_onehot = batch['u_onehot'][:, :max_episode_len]
if self.args.cuda:
states = states.cuda()
states_next = states_next.cuda()
u_onehot = u_onehot.cuda()
hidden_evals = hidden_evals.cuda()
hidden_targets = hidden_targets.cuda()
local_opt_actions = local_opt_actions.cuda()
if hat:
# 神经网络输出的q_eval、q_target、v的维度为(episode_num * max_episode_len, 1)
q_evals = self.eval_joint_q(states, hidden_evals,
local_opt_actions)
q_targets = None
v = None
# 把q_eval维度变回(episode_num, max_episode_len)
q_evals = q_evals.view(episode_num, -1, 1).squeeze(-1)
else:
q_evals = self.eval_joint_q(states, hidden_evals, u_onehot)
q_targets = self.target_joint_q(states_next, hidden_targets,
local_opt_actions)
v = self.v(states, hidden_evals)
# 把q_eval、q_target、v维度变回(episode_num, max_episode_len)
q_evals = q_evals.view(episode_num, -1, 1).squeeze(-1)
q_targets = q_targets.view(episode_num, -1, 1).squeeze(-1)
v = v.view(episode_num, -1, 1).squeeze(-1)
return q_evals, q_targets, v
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_rnn.state_dict(),
self.model_dir + '/' + num + '_rnn_net_params.pkl')
torch.save(self.eval_joint_q.state_dict(),
self.model_dir + '/' + num + '_joint_q_params.pkl')
torch.save(self.v.state_dict(),
self.model_dir + '/' + num + '_v_params.pkl')