class AgentTD3(AgentDDPG):
def __init__(self):
super().__init__()
self.explore_noise = 0.1 # standard deviation of explore noise
self.policy_noise = 0.2 # standard deviation of policy noise
self.update_freq = 2 # delay update frequency
def init(self, net_dim, state_dim, action_dim):
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
self.cri = CriticTwin(net_dim, state_dim, action_dim).to(self.device)
self.cri_target = CriticTwin(net_dim, state_dim,
action_dim).to(self.device)
self.act_optimizer = torch.optim.Adam(self.act.parameters(),
lr=self.learning_rate)
self.cri_optimizer = torch.optim.Adam(self.cri.parameters(),
lr=self.learning_rate)
def update_net(self, buffer, target_step, batch_size,
repeat_times) -> (float, float):
buffer.update_now_len_before_sample()
obj_critic = obj_actor = None
for i in range(int(target_step * repeat_times)):
obj_critic, state = self.get_obj_critic(buffer, batch_size)
self.cri_optimizer.zero_grad()
obj_critic.backward()
self.cri_optimizer.step()
q_value_pg = self.act(state) # policy gradient
obj_actor = -self.cri_target(state, q_value_pg).mean()
self.act_optimizer.zero_grad()
obj_actor.backward()
self.act_optimizer.step()
if i % self.update_freq == 0: # delay update
self.soft_update(self.cri_target, self.cri,
self.soft_update_tau)
self.soft_update(self.act_target, self.act,
self.soft_update_tau)
return obj_actor.item(), obj_critic.item() / 2
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_a = self.act_target.get_action(
next_s, self.policy_noise) # policy noise
next_q = torch.min(*self.cri_target.get_q1_q2(
next_s, next_a)) # twin critics
q_label = reward + mask * next_q
q1, q2 = self.cri.get_q1_q2(state, action)
obj_critic = self.criterion(q1, q_label) + self.criterion(
q2, q_label) # twin critics
return obj_critic, state
class AgentSAC(AgentBase):
def __init__(self):
super().__init__()
self.target_entropy = None
self.alpha_log = None
self.alpha_optimizer = None
def init(self, net_dim, state_dim, action_dim):
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.target_entropy = np.log(action_dim)
self.alpha_log = torch.tensor((-np.log(action_dim) * np.e,), dtype=torch.float32,
requires_grad=True, device=self.device) # trainable parameter
self.act = ActorSAC(net_dim, state_dim, action_dim).to(self.device) # SAC don't use act_target
self.cri = CriticTwin(int(net_dim * 1.25), state_dim, action_dim).to(self.device)
self.cri_target = deepcopy(self.cri)
self.criterion = torch.nn.SmoothL1Loss()
self.act_optimizer = torch.optim.Adam(self.act.parameters(), lr=self.learning_rate)
self.cri_optimizer = torch.optim.Adam(self.cri.parameters(), lr=self.learning_rate)
self.alpha_optimizer = torch.optim.Adam((self.alpha_log,), self.learning_rate)
def select_action(self, state):
states = torch.as_tensor((state,), dtype=torch.float32, device=self.device)
action = self.act.get_action(states)[0]
return action.detach().cpu().numpy()
def update_net(self, buffer, target_step, batch_size, repeat_times):
buffer.update__now_len__before_sample()
alpha = self.alpha_log.exp().detach()
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_a, next_logprob = self.act.get_action_logprob(next_s)
next_q = torch.min(*self.cri_target.get_q1_q2(next_s, next_a))
q_label = reward + mask * (next_q + next_logprob * alpha)
q1, q2 = self.cri.get_q1_q2(state, action)
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)
action_pg, logprob = self.act.get_action_logprob(state) # policy gradient
obj_alpha = (self.alpha_log * (logprob - self.target_entropy).detach()).mean()
self.alpha_optimizer.zero_grad()
obj_alpha.backward()
self.alpha_optimizer.step()
alpha = self.alpha_log.exp().detach()
obj_actor = -(torch.min(*self.cri_target.get_q1_q2(state, action_pg)) + logprob * alpha).mean()
self.act_optimizer.zero_grad()
obj_actor.backward()
self.act_optimizer.step()
return alpha.item(), obj_critic.item()
class AgentTD3(AgentBase):
def __init__(self):
super().__init__()
self.explore_noise = 0.1 # standard deviation of explore noise
self.policy_noise = 0.2 # standard deviation of policy noise
self.update_freq = 2 # delay update frequency
def init(self, net_dim, state_dim, action_dim):
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
self.cri = CriticTwin(net_dim, state_dim, action_dim).to(self.device)
self.cri_target = deepcopy(self.cri)
self.criterion = torch.nn.MSELoss()
self.act_optimizer = torch.optim.Adam(self.act.parameters(),
lr=self.learning_rate)
self.cri_optimizer = torch.optim.Adam(self.cri.parameters(),
lr=self.learning_rate)
def select_action(self, state):
states = torch.as_tensor((state, ),
dtype=torch.float32,
device=self.device).detach_()
action = self.act(states)[0]
action = (action +
torch.randn_like(action) * self.explore_noise).clamp(-1, 1)
return action.cpu().numpy()
def update_net(self, buffer, target_step, batch_size, repeat_times):
buffer.update_now_len_before_sample()
obj_critic = obj_actor = None
for i in range(int(target_step * repeat_times)):
with torch.no_grad():
reward, mask, action, state, next_s = buffer.sample_batch(
batch_size)
next_a = self.act_target.get_action(next_s, self.policy_noise)
next_q = torch.min(*self.cri_target.get_q1_q2(
next_s, next_a)) # twin critics
q_label = reward + mask * next_q
q1, q2 = self.cri.get_q1_q2(state, action)
obj_critic = self.criterion(q1, q_label) + self.criterion(
q2, q_label) # twin critics
self.cri_optimizer.zero_grad()
obj_critic.backward()
self.cri_optimizer.step()
q_value_pg = self.act(state) # policy gradient
obj_actor = -self.cri_target(state, q_value_pg).mean()
self.act_optimizer.zero_grad()
obj_actor.backward()
self.act_optimizer.step()
if i % self.update_freq == 0: # delay update
self.soft_update(self.cri_target, self.cri)
self.soft_update(self.act_target, self.act)
return obj_actor.item(), obj_critic.item() / 2
class AgentTD3(AgentDDPG):
def __init__(self, net_dim, state_dim, action_dim, learning_rate=1e-4):
super().__init__(net_dim, state_dim, action_dim, learning_rate)
self.explore_noise = 0.1 # standard deviation of explore noise
self.policy_noise = 0.2 # standard deviation of policy noise
self.update_freq = 2 # delay update frequency, for soft target update
self.cri = CriticTwin(net_dim, state_dim, action_dim).to(self.device)
self.cri_target = deepcopy(self.cri)
self.optimizer = torch.optim.Adam([{
'params': self.act.parameters(),
'lr': learning_rate
}, {
'params': self.cri.parameters(),
'lr': learning_rate
}])
def update_net(self, buffer, max_step, batch_size, repeat_times):
buffer.update__now_len__before_sample()
obj_critic = obj_actor = None
for i in range(int(max_step * repeat_times)):
with torch.no_grad():
reward, mask, action, state, next_s = buffer.random_sample(
batch_size)
next_a = self.act_target.get_action(
next_s, self.policy_noise) # policy noise
next_q = torch.min(*self.cri_target.get__q1_q2(
next_s, next_a)) # twin critics
q_label = reward + mask * next_q
q1, q2 = self.cri.get__q1_q2(state, action)
obj_critic = self.criterion(q1, q_label) + self.criterion(
q2, q_label) # twin critics
q_value_pg = self.act(state) # policy gradient
obj_actor = -self.cri_target(state, q_value_pg).mean()
obj_united = obj_actor + obj_critic # objective
self.optimizer.zero_grad()
obj_united.backward()
self.optimizer.step()
if i % self.update_freq == 0: # delay update
soft_target_update(self.cri_target, self.cri)
soft_target_update(self.act_target, self.act)
return obj_actor.item(), obj_critic.item() / 2
class AgentSAC(AgentBase):
def __init__(self, net_dim, state_dim, action_dim, learning_rate=1e-4):
super().__init__()
self.target_entropy = np.log(action_dim)
self.alpha_log = torch.tensor(
(-np.log(action_dim) * np.e, ),
dtype=torch.float32,
requires_grad=True,
device=self.device) # trainable parameter
self.act = ActorSAC(net_dim, state_dim, action_dim).to(self.device)
self.act_target = deepcopy(self.act)
self.cri = CriticTwin(
int(net_dim * 1.25),
state_dim,
action_dim,
).to(self.device)
self.cri_target = deepcopy(self.cri)
self.criterion = torch.nn.SmoothL1Loss()
self.optimizer = torch.optim.Adam([{
'params': self.act.parameters(),
'lr': learning_rate * 0.75
}, {
'params': self.cri.parameters(),
'lr': learning_rate * 1.25
}, {
'params': (self.alpha_log, ),
'lr': learning_rate
}])
def select_actions(self, states): # states = (state, ...)
states = torch.as_tensor(states,
dtype=torch.float32,
device=self.device)
actions = self.act.get_action(states)
return actions.detach().cpu().numpy()
def update_net(self, buffer, max_step, batch_size, repeat_times):
buffer.update__now_len__before_sample()
alpha = self.alpha_log.exp().detach()
obj_actor = obj_critic = None
for _ in range(int(max_step * repeat_times)):
with torch.no_grad():
reward, mask, action, state, next_s = buffer.random_sample(
batch_size)
next_a, next_log_prob = self.act_target.get__action__log_prob(
next_s)
next_q = torch.min(*self.cri_target.get__q1_q2(next_s, next_a))
q_label = reward + mask * (next_q + next_log_prob * alpha)
q1, q2 = self.cri.get__q1_q2(state, action)
obj_critic = self.criterion(q1, q_label) + self.criterion(
q2, q_label)
action_pg, log_prob = self.act.get__action__log_prob(
state) # policy gradient
obj_alpha = (self.alpha_log *
(log_prob - self.target_entropy).detach()).mean()
alpha = self.alpha_log.exp().detach()
with torch.no_grad():
self.alpha_log[:] = self.alpha_log.clamp(-16, 2)
obj_actor = -(
torch.min(*self.cri_target.get__q1_q2(state, action_pg)) +
log_prob * alpha).mean()
obj_united = obj_critic + obj_alpha + obj_actor
self.optimizer.zero_grad()
obj_united.backward()
self.optimizer.step()
soft_target_update(self.cri_target, self.cri)
soft_target_update(self.act_target, self.act)
# return obj_actor.item(), obj_critic.item()
return alpha.item(), obj_critic.item()