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
actor_input_shape = self.obs_shape # actor网络输入的维度，和vdn、qmix的rnn输入维度一样，使用同一个网络结构
critic_input_shape = self.state_shape # critic网络输入的维度
# 根据参数决定RNN的输入维度
if args.last_action:
actor_input_shape += self.n_actions
if args.reuse_network:
actor_input_shape += self.n_agents
self.args = args
# 神经网络
# 每个agent选动作的网络,输出当前agent所有动作对应的概率，用该概率选动作的时候还需要用softmax再运算一次。
if self.args.alg == 'central_v':
self.eval_rnn = RNN(actor_input_shape, args)
print('Init alg central_v')
elif self.args.alg == 'central_v+commnet':
self.eval_rnn = CommNet(actor_input_shape, args)
print('Init alg central_v+commnet')
elif self.args.alg == 'central_v+g2anet':
print('Init alg central_v+g2anet')
self.eval_rnn = G2ANet(actor_input_shape, args)
else:
raise Exception("No such algorithm")
self.eval_critic = Critic(critic_input_shape, self.args)
self.target_critic = Critic(critic_input_shape, self.args)
if self.args.cuda:
self.eval_rnn.cuda()
self.eval_critic.cuda()
self.target_critic.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_params.pkl'):
path_rnn = self.model_dir + '/rnn_params.pkl'
path_critic = self.model_dir + '/critic_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_critic.load_state_dict(
torch.load(path_critic, map_location=map_location))
print('Successfully load the model: {} and {}'.format(
path_rnn, path_critic))
else:
raise Exception("No model!")
# 让target_net和eval_net的网络参数相同
self.target_critic.load_state_dict(self.eval_critic.state_dict())
self.rnn_parameters = list(self.eval_rnn.parameters())
self.critic_parameters = list(self.eval_critic.parameters())
if args.optimizer == "RMS":
self.critic_optimizer = torch.optim.RMSprop(self.critic_parameters,
lr=args.lr_critic)
self.rnn_optimizer = torch.optim.RMSprop(self.rnn_parameters,
lr=args.lr_actor)
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
self.eval_hidden = None
class CentralV:
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
actor_input_shape = self.obs_shape # actor网络输入的维度，和vdn、qmix的rnn输入维度一样，使用同一个网络结构
critic_input_shape = self.state_shape # critic网络输入的维度
# 根据参数决定RNN的输入维度
if args.last_action:
actor_input_shape += self.n_actions
if args.reuse_network:
actor_input_shape += self.n_agents
self.args = args
# 神经网络
# 每个agent选动作的网络,输出当前agent所有动作对应的概率，用该概率选动作的时候还需要用softmax再运算一次。
if self.args.alg == 'central_v':
self.eval_rnn = RNN(actor_input_shape, args)
print('Init alg central_v')
elif self.args.alg == 'central_v+commnet':
self.eval_rnn = CommNet(actor_input_shape, args)
print('Init alg central_v+commnet')
elif self.args.alg == 'central_v+g2anet':
print('Init alg central_v+g2anet')
self.eval_rnn = G2ANet(actor_input_shape, args)
else:
raise Exception("No such algorithm")
self.eval_critic = Critic(critic_input_shape, self.args)
self.target_critic = Critic(critic_input_shape, self.args)
if self.args.cuda:
self.eval_rnn.cuda()
self.eval_critic.cuda()
self.target_critic.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_params.pkl'):
path_rnn = self.model_dir + '/rnn_params.pkl'
path_critic = self.model_dir + '/critic_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_critic.load_state_dict(
torch.load(path_critic, map_location=map_location))
print('Successfully load the model: {} and {}'.format(
path_rnn, path_critic))
else:
raise Exception("No model!")
# 让target_net和eval_net的网络参数相同
self.target_critic.load_state_dict(self.eval_critic.state_dict())
self.rnn_parameters = list(self.eval_rnn.parameters())
self.critic_parameters = list(self.eval_critic.parameters())
if args.optimizer == "RMS":
self.critic_optimizer = torch.optim.RMSprop(self.critic_parameters,
lr=args.lr_critic)
self.rnn_optimizer = torch.optim.RMSprop(self.rnn_parameters,
lr=args.lr_actor)
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
self.eval_hidden = None
def learn(self, batch, max_episode_len, train_step,
epsilon): # train_step表示是第几次学习，用来控制更新target_net网络的参数
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, terminated = batch['u'], batch['r'], batch[
'avail_u'], 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()
mask = mask.cuda()
# 训练critic网络，并且得到每条经验的td_error, (episode_num, max_episode_len, 1)
td_error = self._train_critic(batch, max_episode_len, train_step)
td_error = td_error.repeat(1, 1, self.n_agents)
# 每个agent的所有动作的概率 (episode_num, max_episode_len， n_agents，n_actions)
action_prob = self._get_action_prob(batch, max_episode_len, epsilon)
# 每个agent的选择的动作对应的概率 (episode_num, max_episode_len， n_agents)
pi_taken = torch.gather(action_prob, dim=3, index=u).squeeze(3)
pi_taken[mask ==
0] = 1.0 # 因为要取对数，对于那些填充的经验，所有概率都为0，取了log就是负无穷了，所以让它们变成1
log_pi_taken = torch.log(pi_taken)
# loss函数，(episode_num, max_episode_len, n_agents)
loss = -((td_error.detach() * log_pi_taken) * mask).sum() / mask.sum()
self.rnn_optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(self.rnn_parameters,
self.args.grad_norm_clip)
self.rnn_optimizer.step()
# print('Actor loss is', loss)
def _get_v_values(self, batch, max_episode_len):
v_evals, v_targets = [], []
for transition_idx in range(max_episode_len):
inputs, inputs_next = batch['s'][:, transition_idx], batch[
's_next'][:, transition_idx],
if self.args.cuda:
inputs = inputs.cuda()
inputs_next = inputs_next.cuda()
# 神经网络输入的是(episode_num , state_shape)二维数据，得到的是(episode_num ， 1)二维数据
v_eval = self.eval_critic(inputs)
v_target = self.target_critic(inputs_next)
v_evals.append(v_eval)
v_targets.append(v_target)
v_evals = torch.stack(v_evals,
dim=1) # (episode_num, max_episode_len, 1)
v_targets = torch.stack(v_targets, dim=1)
return v_evals, v_targets
def _get_actor_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, u_onehot = batch['o'][:, transition_idx], batch['u_onehot'][:]
episode_num = obs.shape[0]
inputs = []
inputs.append(obs)
# 给inputs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])
if self.args.reuse_network:
# 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))
# 要把inputs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)
return inputs
def _get_action_prob(self, batch, max_episode_len, epsilon):
episode_num = batch['o'].shape[0]
avail_actions = batch[
'avail_u'] # coma不用target_actor，所以不需要最后一个obs的下一个可执行动作
action_prob = []
for transition_idx in range(max_episode_len):
inputs = self._get_actor_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()
outputs, 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)
# 把q_eval维度重新变回(8, 5,n_actions)
outputs = outputs.view(episode_num, self.n_agents, -1)
prob = torch.nn.functional.softmax(outputs, dim=-1)
action_prob.append(prob)
# 得的action_prob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
action_prob = torch.stack(action_prob, dim=1).cpu()
action_num = avail_actions.sum(dim=-1, keepdim=True).float().repeat(
1, 1, 1, avail_actions.shape[-1]) # 可以选择的动作的个数
action_prob = ((1 - epsilon) * action_prob +
torch.ones_like(action_prob) * epsilon / action_num)
action_prob[avail_actions == 0] = 0.0 # 不能执行的动作概率为0
# 因为上面把不能执行的动作概率置为0，所以概率和不为1了，这里要重新正则化一下。执行过程中Categorical会自己正则化。
action_prob = action_prob / action_prob.sum(dim=-1, keepdim=True)
# 因为有许多经验是填充的，它们的avail_actions都填充的是0，所以该经验上所有动作的概率都为0，在正则化的时候会得到nan。
# 因此需要再一次将该经验对应的概率置为0
action_prob[avail_actions == 0] = 0.0
if self.args.cuda:
action_prob = action_prob.cuda()
return action_prob
def init_hidden(self, episode_num):
# 为每个episode中的每个agent都初始化一个eval_hidden
self.eval_hidden = torch.zeros(
(episode_num, self.n_agents, self.args.rnn_hidden_dim))
def _train_critic(self, batch, max_episode_len, train_step):
r, terminated = batch['r'], 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:
mask = mask.cuda()
r = r.cuda()
terminated = terminated.cuda()
v_evals, v_next_target = self._get_v_values(batch, max_episode_len)
targets = r + self.args.gamma * v_next_target * (1 - terminated)
td_error = targets.detach() - v_evals
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()
# print('Critic Loss is ', loss)
self.critic_optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(self.critic_parameters,
self.args.grad_norm_clip)
self.critic_optimizer.step()
if train_step > 0 and train_step % self.args.target_update_cycle == 0:
self.target_critic.load_state_dict(self.eval_critic.state_dict())
return td_error
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_critic.state_dict(),
self.model_dir + '/' + num + '_critic_params.pkl')
torch.save(self.eval_rnn.state_dict(),
self.model_dir + '/' + num + '_rnn_params.pkl')