class AgentDoubleDQN(AgentDQN):
def __init__(self):
super().__init__()
self.explore_rate = 0.25 # the probability of choosing action randomly in epsilon-greedy
self.softmax = torch.nn.Softmax(dim=1)
def init(self, net_dim, state_dim, action_dim):
self.action_dim = action_dim
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
self.cri = QNetTwin(net_dim, state_dim, action_dim).to(self.device)
self.cri_target = deepcopy(self.cri)
self.act = self.cri
self.criterion = torch.nn.SmoothL1Loss()
self.cri_optimizer = torch.optim.Adam(self.act.parameters(),
lr=self.learning_rate)
def select_action(self, state): # for discrete action space
states = torch.as_tensor((state, ),
dtype=torch.float32,
device=self.device).detach_()
actions = self.act(states)
if rd.rand() < self.explore_rate: # epsilon-greedy
action = self.softmax(actions)[0]
a_prob = action.detach().cpu().numpy(
) # choose action according to Q value
a_int = rd.choice(self.action_dim, p=a_prob)
else:
action = actions[0]
a_int = action.argmax(dim=0).cpu().numpy()
return a_int
def update_net(self, buffer, target_step, batch_size, repeat_times):
buffer.update_now_len_before_sample()
next_q = obj_critic = None
for _ in range(int(target_step * repeat_times)):
with torch.no_grad():
reward, mask, action, state, next_s = buffer.sample_batch(
batch_size)
next_q = torch.min(*self.cri_target.get_q1_q2(next_s))
next_q = next_q.max(dim=1, keepdim=True)[0]
q_label = reward + mask * next_q
act_int = action.type(torch.long)
q1, q2 = [
qs.gather(1, act_int) for qs in self.cri.get_q1_q2(state)
]
obj_critic = self.criterion(q1, q_label) + self.criterion(
q2, q_label)
self.cri_optimizer.zero_grad()
obj_critic.backward()
self.cri_optimizer.step()
self.soft_update(self.cri_target, self.cri)
return next_q.mean().item(), obj_critic.item() / 2
class AgentDoubleDQN(AgentDQN):
def __init__(self):
super().__init__()
self.explore_rate = 0.25 # the probability of choosing action randomly in epsilon-greedy
self.softmax = torch.nn.Softmax(dim=1)
def init(self, net_dim, state_dim, action_dim):
self.action_dim = action_dim
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
self.cri = QNetTwin(net_dim, state_dim, action_dim).to(self.device)
self.cri_target = QNetTwin(net_dim, state_dim,
action_dim).to(self.device)
self.act = self.cri
self.cri_optimizer = torch.optim.Adam(self.act.parameters(),
lr=self.learning_rate)
def select_action(self, state) -> np.ndarray: # for discrete action space
states = torch.as_tensor((state, ),
dtype=torch.float32,
device=self.device).detach_()
actions = self.act(states)
if rd.rand() < self.explore_rate: # epsilon-greedy
action = self.softmax(actions)[0]
a_prob = action.detach().cpu().numpy(
) # choose action according to Q value
a_int = rd.choice(self.action_dim, p=a_prob)
else:
action = actions[0]
a_int = action.argmax(dim=0).cpu().numpy()
return a_int
def get_obj_critic(self, buffer,
batch_size) -> (torch.Tensor, torch.Tensor):
with torch.no_grad():
reward, mask, action, state, next_s = buffer.sample_batch(
batch_size)
next_q = torch.min(*self.cri_target.get_q1_q2(next_s))
next_q = next_q.max(dim=1, keepdim=True)[0]
q_label = reward + mask * next_q
act_int = action.type(torch.long)
q1, q2 = [qs.gather(1, act_int) for qs in self.act.get_q1_q2(state)]
obj_critic = self.criterion(q1, q_label) + self.criterion(q2, q_label)
return obj_critic, q1