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._get_critic_input_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。
self.eval_rnn = RNN(actor_input_shape, args)
# 得到当前agent的所有可执行动作对应的联合Q值，得到之后需要用该Q值和actor网络输出的概率计算advantage
self.eval_critic = ComaCritic(critic_input_shape, self.args)
self.target_critic = ComaCritic(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 os.path.exists(self.model_dir + '/rnn_params.pkl'):
path_rnn = self.model_dir + '/rnn_params.pkl'
path_coma = self.model_dir + '/critic_params.pkl'
self.eval_rnn.load_state_dict(torch.load(path_rnn))
self.eval_critic.load_state_dict(torch.load(path_coma))
print('Successfully load the model: {} and {}'.format(
path_rnn, path_coma))
# 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 __init__(self, args):
self.n_agents = args.n_agents
self.n_actions = args.n_actions
self.obs_shape = args.obs_shape
actor_input_shape = self.obs_shape
# Actor Network (RNN)
if args.last_action:
actor_input_shape += self.n_actions
if args.reuse_networks:
actor_input_shape += self.n_agents
self.eval_rnn = RNN(actor_input_shape, args)
print('Init Algo Coma')
self.eval_critic = ComaCritic(args)
self.target_critic = ComaCritic(args)
if args.use_cuda and torch.cuda.is_available():
self.device = torch.device("cuda:0")
self.eval_rnn.to(self.device)
self.eval_critic.to(self.device)
self.target_critic.to(self.device)
else:
self.device = torch.device("cpu")
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())
self.critic_optimizer = torch.optim.Adam(self.critic_parameters,
lr=args.critic_lr)
self.rnn_optimizer = torch.optim.Adam(self.rnn_parameters,
lr=args.actor_lr)
self.args = args
self.loss_func = torch.nn.MSELoss()
self.eval_hidden = None
class COMA:
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._get_critic_input_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 == 'coma':
print('Init alg coma')
self.eval_rnn = RNN(actor_input_shape, args)
elif self.args.alg == 'coma+commnet':
print('Init alg coma+commnet')
self.eval_rnn = CommNet(actor_input_shape, args)
elif self.args.alg == 'coma+g2anet':
print('Init alg coma+g2anet')
self.eval_rnn = G2ANet(actor_input_shape, args)
else:
raise Exception("No such algorithm")
# 得到当前agent的所有可执行动作对应的联合Q值，得到之后需要用该Q值和actor网络输出的概率计算advantage
self.eval_critic = ComaCritic(critic_input_shape, self.args)
self.target_critic = ComaCritic(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 os.path.exists(self.model_dir + '/rnn_params.pkl'):
# path_rnn = self.model_dir + '/rnn_params.pkl'
# path_coma = self.model_dir + '/critic_params.pkl'
# self.eval_rnn.load_state_dict(torch.load(path_rnn))
# self.eval_critic.load_state_dict(torch.load(path_coma))
# print('Successfully load the model: {} and {}'.format(path_rnn, path_coma))
# 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 _get_critic_input_shape(self):
# state
input_shape = self.state_shape # 48
# obs
input_shape += self.obs_shape # 30
# agent_id
input_shape += self.n_agents # 3
# 所有agent的当前动作和上一个动作
input_shape += self.n_actions * self.n_agents * 2 # 54
return input_shape
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()
# 根据经验计算每个agent的Ｑ值,从而跟新Critic网络。然后计算各个动作执行的概率，从而计算advantage去更新Actor。
q_values = self._train_critic(
batch, max_episode_len,
train_step) # 训练critic网络，并且得到每个agent的所有动作的Ｑ值
action_prob = self._get_action_prob(batch, max_episode_len,
epsilon) # 每个agent的所有动作的概率
q_taken = torch.gather(q_values, dim=3,
index=u).squeeze(3) # 每个agent的选择的动作对应的Ｑ值
pi_taken = torch.gather(action_prob, dim=3,
index=u).squeeze(3) # 每个agent的选择的动作对应的概率
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)
# 计算advantage
baseline = (q_values * action_prob).sum(
dim=3, keepdim=True).squeeze(3).detach()
advantage = (q_taken - baseline).detach()
loss = -((advantage * 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('Training: loss is', loss.item())
# print('Training: critic params')
# for params in self.eval_critic.named_parameters():
# print(params)
# print('Training: actor params')
# for params in self.eval_rnn.named_parameters():
# print(params)
def _get_critic_inputs(self, batch, transition_idx, max_episode_len):
# 取出所有episode上该transition_idx的经验
obs, obs_next, s, s_next = batch['o'][:, transition_idx], batch['o_next'][:, transition_idx],\
batch['s'][:, transition_idx], batch['s_next'][:, transition_idx]
u_onehot = batch['u_onehot'][:, transition_idx]
if transition_idx != max_episode_len - 1:
u_onehot_next = batch['u_onehot'][:, transition_idx + 1]
else:
u_onehot_next = torch.zeros(*u_onehot.shape)
# s和s_next是二维的，没有n_agents维度，因为所有agent的s一样。其他都是三维的，到时候不能拼接，所以要把s转化成三维的
s = s.unsqueeze(1).expand(-1, self.n_agents, -1)
s_next = s_next.unsqueeze(1).expand(-1, self.n_agents, -1)
episode_num = obs.shape[0]
# 因为coma的critic用到的是所有agent的动作，所以要把u_onehot最后一个维度上当前agent的动作变成所有agent的动作
u_onehot = u_onehot.view(
(episode_num, 1, -1)).repeat(1, self.n_agents, 1)
u_onehot_next = u_onehot_next.view(
(episode_num, 1, -1)).repeat(1, self.n_agents, 1)
if transition_idx == 0: # 如果是第一条经验，就让前一个动作为0向量
u_onehot_last = torch.zeros_like(u_onehot)
else:
u_onehot_last = batch['u_onehot'][:, transition_idx - 1]
u_onehot_last = u_onehot_last.view(
(episode_num, 1, -1)).repeat(1, self.n_agents, 1)
inputs, inputs_next = [], []
# 添加状态
inputs.append(s)
inputs_next.append(s_next)
# 添加obs
inputs.append(obs)
inputs_next.append(obs_next)
# 添加所有agent的上一个动作
inputs.append(u_onehot_last)
inputs_next.append(u_onehot)
# 添加当前动作
'''
因为coma对于当前动作，输入的是其他agent的当前动作，不输入当前agent的动作，为了方便起见，每次虽然输入当前agent的
当前动作，但是将其置为0相量，也就相当于没有输入。
'''
action_mask = (1 - torch.eye(self.n_agents)) # th.eye（）生成一个二维对角矩阵
# 得到一个矩阵action_mask，用来将(episode_num, n_agents, n_agents * n_actions)的actions中每个agent自己的动作变成0向量
action_mask = action_mask.view(-1, 1).repeat(1, self.n_actions).view(
self.n_agents, -1)
inputs.append(u_onehot * action_mask.unsqueeze(0))
inputs_next.append(u_onehot_next * action_mask.unsqueeze(0))
# 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
'''
inputs.append(
torch.eye(self.n_agents).unsqueeze(0).expand(episode_num, -1, -1))
inputs_next.append(
torch.eye(self.n_agents).unsqueeze(0).expand(episode_num, -1, -1))
# 要把inputs中的5项输入拼起来，并且要把其维度从(episode_num, n_agents, inputs)三维转换成(episode_num * n_agents, inputs)二维
inputs = torch.cat(
[x.reshape(episode_num * self.n_agents, -1) for x in inputs],
dim=1)
inputs_next = torch.cat(
[x.reshape(episode_num * self.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_critic_inputs(
batch, transition_idx, max_episode_len)
if self.args.cuda:
inputs = inputs.cuda()
inputs_next = inputs_next.cuda()
# 神经网络输入的是(episode_num * n_agents, inputs)二维数据，得到的是(episode_num * n_agents， n_actions)二维数据
q_eval = self.eval_critic(inputs)
q_target = self.target_critic(inputs_next)
# 把q值的维度重新变回(episode_num, n_agents, 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_evals和q_targets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 _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)
# TODO 检查inputs_next是不是相当于inputs向后移动一条
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 = self.eval_rnn.init_hidden().unsqueeze(0).expand(
episode_num, self.n_agents, -1)
def _train_critic(self, batch, max_episode_len, train_step):
u, r, avail_u, terminated = batch['u'], batch['r'], batch[
'avail_u'], batch['terminated']
u_next = u[:, 1:]
padded_u_next = torch.zeros(*u[:, -1].shape,
dtype=torch.long).unsqueeze(1)
u_next = torch.cat((u_next, padded_u_next), dim=1)
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()
u_next = u_next.cuda()
mask = mask.cuda()
# 得到每个agent对应的Q值，维度为(episode个数, max_episode_len， n_agents，n_actions)
# q_next_target为下一个状态-动作对应的target网络输出的Q值，没有包括reward
q_evals, q_next_target = self._get_q_values(batch, max_episode_len)
q_values = q_evals.clone() # 在函数的最后返回，用来计算advantage从而更新actor
# 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)
q_next_target = torch.gather(q_next_target, dim=3,
index=u_next).squeeze(3)
targets = td_lambda_target(batch, max_episode_len, q_next_target.cpu(),
self.args)
if self.args.cuda:
targets = targets.cuda()
td_error = targets.detach() - q_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('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 q_values
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')
class COMA:
def __init__(self, args):
self.n_agents = args.n_agents
self.n_actions = args.n_actions
self.obs_shape = args.obs_shape
actor_input_shape = self.obs_shape
# Actor Network (RNN)
if args.last_action:
actor_input_shape += self.n_actions
if args.reuse_networks:
actor_input_shape += self.n_agents
self.eval_rnn = RNN(actor_input_shape, args)
print('Init Algo Coma')
self.eval_critic = ComaCritic(args)
self.target_critic = ComaCritic(args)
if args.use_cuda and torch.cuda.is_available():
self.device = torch.device("cuda:0")
self.eval_rnn.to(self.device)
self.eval_critic.to(self.device)
self.target_critic.to(self.device)
else:
self.device = torch.device("cpu")
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())
self.critic_optimizer = torch.optim.Adam(self.critic_parameters,
lr=args.critic_lr)
self.rnn_optimizer = torch.optim.Adam(self.rnn_parameters,
lr=args.actor_lr)
self.args = args
self.loss_func = torch.nn.MSELoss()
self.eval_hidden = None
def learn(self, batch, max_episode_len, train_step, epsilon):
bs = batch['o'].shape[0]
self.init_hidden(bs)
for key in batch.keys():
if key == 'u':
batch[key] = torch.tensor(batch[key], dtype=torch.long)
else:
batch[key] = torch.tensor(batch[key], dtype=torch.float32)
u, r, terminated = batch['u'], batch['r'], batch['terminated']
if self.args.use_cuda:
u = u.to(self.device)
critic_rets = self._train_critic(batch, train_step)
q_taken, q_values = [], []
for a_i, (q_eval, q_all) in zip(range(self.n_agents), critic_rets):
q_taken.append(q_eval)
q_values.append(q_all)
q_taken = torch.stack(q_taken, dim=2).squeeze(3)
q_values = torch.stack(q_values, dim=2)
action_prob = self._get_action_prob(batch, max_episode_len, epsilon)
pi_taken = torch.gather(action_prob, dim=3, index=u).squeeze(3)
log_pi_taken = torch.log(pi_taken)
# Advantage for actor(policy) optimization
baseline = (q_values * action_prob).sum(
dim=3, keepdim=True).squeeze(3).detach()
advantage = (q_taken - baseline).detach()
loss = -(advantage * log_pi_taken).mean()
self.rnn_optimizer.zero_grad()
disable_gradients(self.eval_critic)
loss.backward()
enable_gradients(self.eval_critic)
torch.nn.utils.clip_grad_norm_(self.rnn_parameters,
self.args.grad_norm_clip)
self.rnn_optimizer.step()
def _train_critic(self, batch, train_step):
"""
Unlike the qmix or vdn which seems like q_learning to choose the argmax Q values as the q_targets
COMA is someway like the MADDPG or DDPG algorithm which is deterministic policy gradient method
So it requires the deterministic next action infos as 'u_next'
:return: [n_agents * [(bs, episode_limit, 1), (bs, episode_limit, n_actions)]]
"""
r, terminated = batch['r'], batch['terminated']
if self.args.use_cuda:
r = r.to(self.device)
terminated = terminated.to(self.device)
critic_in, target_critic_in = self._get_critic_inputs(batch)
q_targets = self.target_critic(
target_critic_in) # n_agents * (bs, episode_limit, 1)
critic_rets = self.eval_critic(critic_in, return_all_q=True)
q_loss = 0
for a_i, q_target, (q_eval, q_all) in zip(range(self.n_agents),
q_targets, critic_rets):
target = r + self.args.gamma * q_target * (1 - terminated)
q_loss += self.loss_func(target, q_eval)
self.critic_optimizer.zero_grad()
q_loss.backward()
self.eval_critic.scale_shared_grads()
torch.nn.utils.clip_grad_norm_(
self.eval_critic.parameters(),
self.args.grad_norm_clip * self.n_agents)
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 critic_rets
def _get_critic_inputs(self, batch):
"""
The COMA algorithm handle the critic inputs with total steps (without transition_idx)
"""
obs, obs_next = batch['o'], batch[
'o_next'] # (bs, episode_limit, n_agents, obs_shape)
u_onehot = batch[
'u_onehot'] # (bs, episode_limit, n_agents, n_actions)
u_onehot_next = u_onehot[:,
1:] # (bs, episode_limit - 1, n_agents, n_actions)
padded_next = torch.zeros(*u_onehot[:, -1].shape,
dtype=torch.float32).unsqueeze(
1) # Add a step with zeros
u_onehot_next = torch.cat((u_onehot_next, padded_next), dim=1)
if self.args.use_cuda:
obs = obs.to(self.device)
obs_next = obs_next.to(self.device)
u_onehot = u_onehot.to(self.device)
u_onehot_next = u_onehot_next.to(self.device)
agents_obs, agents_obs_next = [], []
agents_u, agents_u_next = [], []
for a_i in range(self.n_agents):
agent_obs, agent_obs_next = obs[:, :,
a_i], obs_next[:, :,
a_i] # (bs, episode_limit, obs_shape)
agent_u, agent_u_next = u_onehot[:, :,
a_i], u_onehot_next[:, :,
a_i] # (bs, episode_limit, n_actions)
agents_obs.append(agent_obs)
agents_obs_next.append(agent_obs_next)
agents_u.append(agent_u)
agents_u_next.append(agent_u_next)
target_critic_in = list(zip(agents_obs_next, agents_u_next))
critic_in = list(zip(agents_obs, agents_u))
return critic_in, target_critic_in
def _get_actor_inputs(self, batch, transition_idx):
# Because the rnn agent actor network didn't initialize a target network, it requires none next infos
obs, u_onehot = batch['o'][:, transition_idx], batch['u_onehot'][:]
bs = obs.shape[0]
# Observation
inputs = [obs]
if self.args.last_action:
if transition_idx == 0:
inputs.append(torch.zeros_like(u_onehot[:, transition_idx]))
else:
inputs.append(u_onehot[:, transition_idx - 1])
if self.args.reuse_networks:
inputs.append(
torch.eye(self.args.n_agents).unsqueeze(0).expand(bs, -1, -1))
# Since the using of GRU network, the inputs shape should be shaped as 2 dimensions
inputs = torch.cat(
[x.reshape(bs * self.args.n_agents, -1) for x in inputs], dim=1)
return inputs
def _get_action_prob(self, batch, max_episode_len, epsilon):
bs = batch['o'].shape[0]
# The available actions for each agent. In MPE, an agent could choose every action at any time-step.
avail_actions = torch.ones_like(
batch['u_onehot']
) # (bs, episode_limit, n_agents, n_actions) --> all 1
action_prob = []
for transition_idx in range(max_episode_len):
inputs = self._get_actor_inputs(batch, transition_idx)
if self.args.use_cuda:
inputs = inputs.to(self.device)
self.eval_hidden = self.eval_hidden.to(self.device)
outputs, self.eval_hidden = self.eval_rnn(
inputs, self.eval_hidden) # outputs:(bs * n_agents, n_actions)
outputs = outputs.view(bs, self.n_agents, -1)
prob = torch.nn.functional.softmax(outputs, dim=-1)
action_prob.append(prob)
action_prob = torch.stack(
action_prob,
dim=1).cpu() # (bs, episode_limit, n_agents, n_actions)
actions_num = avail_actions.sum(dim=-1, keepdim=True).float().repeat(
1, 1, 1, avail_actions.shape[-1])
action_prob = (1 - epsilon) * action_prob + torch.ones_like(
action_prob) * epsilon / actions_num
action_prob = action_prob / action_prob.sum(dim=-1, keepdim=True)
if self.args.use_cuda:
action_prob = action_prob.to(self.device)
return action_prob
def _td_lambda_target(self, batch, max_episode_len, q_targets):
bs = batch['o'].shape[0]
terminated = (1 - batch["terminated"].float()).repeat(
1, 1, self.n_agents)
r = batch['r'].repeat(
(1, 1, self.n_agents
)) # (bs, episode_limit, 1) --> (bs, episode_limit, n_agents)
n_step_returns = torch.zeros(
(bs, max_episode_len, self.n_agents, max_episode_len))
for transition_idx in range(max_episode_len - 1, -1, -1):
n_step_returns[:, transition_idx, :, 0] = r[:, transition_idx] + self.args.gamma *\
q_targets[:, transition_idx] * terminated[:, transition_idx]
for n in range(1, max_episode_len - transition_idx):
n_step_returns[:, transition_idx, :, n] = r[:, transition_idx] + self.args.gamma *\
n_step_returns[:, transition_idx + 1, :, n - 1]
lambda_return = torch.zeros((bs, max_episode_len, self.n_agents))
for transition_idx in range(max_episode_len):
returns = torch.zeros((bs, self.n_agents))
for n in range(1, max_episode_len - transition_idx):
returns += pow(self.args.td_lambda, n -
1) * n_step_returns[:, transition_idx, :, n - 1]
lambda_return[:, transition_idx] = (1 - self.args.td_lambda) * returns +\
pow(self.args.td_lambda, max_episode_len - transition_idx - 1) *\
n_step_returns[:, transition_idx, :, max_episode_len - transition_idx - 1]
return lambda_return
def init_hidden(self, batch_size):
self.eval_hidden = torch.zeros(
(batch_size, self.n_agents, self.args.rnn_hidden_dim))
def get_params(self):
return {
'eval_critic': self.eval_critic.state_dict(),
'eval_rnn': self.eval_rnn.state_dict()
}
def load_params(self, params_dict):
# Get parameters from save_dict
self.eval_rnn.load_state_dict(params_dict['eval_rnn'])
self.eval_critic.load_state_dict(params_dict['eval_critic'])
# Copy the eval networks to target networks
self.target_critic.load_state_dict(self.target_critic.state_dict())