Why discrete the action?

[xueliu8617112]

Hello, Dr.Haydari. I am a beginner in constraint RL. Through reading your code, I found you discrete the action. Can you tell me the reason? Thanks!

[ammarhydr]

Hello Xue Liu, In RL, MDP formulation can be discrete or continuous depending on the control environment and agent design. In this case, I applied SAC in the discrete action domain on the Cartpole game. There is a good summary of discrete implementation for SAC in this paper: https://arxiv.org/abs/1910.07207

On Wed, Dec 7, 2022 at 6:48 AM Xue Liu @.***> wrote:

Hello, Dr.Haydari. I am a beginner in constraint RL. Through reading your code, I found you discrete the action. Can you tell me the reason? Thanks!

— Reply to this email directly, view it on GitHub https://github.com/ammarhydr/SAC-Lagrangian/issues/3, or unsubscribe https://github.com/notifications/unsubscribe-auth/ABMOULKA6WHTAV3A6VTGDZDWMCPUTANCNFSM6AAAAAASW47T5M . You are receiving this because you are subscribed to this thread.Message ID: @.***>

--

Ammar Haydari PhD Student UC Davis

[xueliu8617112]

Hello, Dr.Haydari. Thanks for replying. Since the original action space is continuous, is it superfluous to do this? However, when i change the code to adapt the original continuous action space. It didn't work well. Here is the code revised.

`

import os import torch import torch.nn.functional as F from torch.optim import Adam from utils import soft_update, hard_update from modelrul import GaussianPolicy, QNetwork, DeterministicPolicy import numpy as np

class SAC(object): def init(self, num_inputs, action_space, args, lambda_init = 1.):

    self.gamma = args.gamma
    self.tau = args.tau
    self.alpha = args.alpha

    self.policy_type = args.policy
    self.target_update_interval = args.target_update_interval
    self.lab_update_interval = 12
    self.automatic_entropy_tuning = args.automatic_entropy_tuning

    self.device = torch.device("cuda" if args.cuda else "cpu")

    self.critic = QNetwork(num_inputs, action_space.shape[0], args.hidden_size).to(device=self.device)
    self.critic_optim = Adam(self.critic.parameters(), lr=args.lr)
    self.critic_target = QNetwork(num_inputs, action_space.shape[0], args.hidden_size).to(device=self.device)
    hard_update(self.critic_target, self.critic)

    self.critic_c = QNetwork(num_inputs, action_space.shape[0], args.hidden_size).to(device=self.device)
    self.critic_optim_c = Adam(self.critic.parameters(), lr=args.lr)
    self.critic_target_c = QNetwork(num_inputs, action_space.shape[0], args.hidden_size).to(device=self.device)
    hard_update(self.critic_target_c, self.critic_c)

    self.lam = torch.tensor(lambda_init, requires_grad=True)
    self.lam_optimiser = torch.optim.Adam([self.lam], lr=3e-4)

    self.cost_lim = -5e-4

    if self.policy_type == "Gaussian":
        # Target Entropy = −dim(A) (e.g. , -6 for HalfCheetah-v2) as given in the paper
        if self.automatic_entropy_tuning is True:
            self.target_entropy = -torch.prod(torch.Tensor(action_space.shape).to(self.device)).item()
            self.log_alpha = torch.zeros(1, requires_grad=True, device=self.device)
            self.alpha_optim = Adam([self.log_alpha], lr=args.lr)

        self.policy = GaussianPolicy(num_inputs, action_space.shape[0], args.hidden_size, action_space).to(self.device)
        self.policy_optim = Adam(self.policy.parameters(), lr=args.lr)

    else:
        self.alpha = 0
        self.automatic_entropy_tuning = False
        self.policy = DeterministicPolicy(num_inputs, action_space.shape[0], args.hidden_size, action_space).to(self.device)
        self.policy_optim = Adam(self.policy.parameters(), lr=args.lr)

def select_action(self, state, evaluate=False):
    state = torch.FloatTensor(state).to(self.device).unsqueeze(0)
    if evaluate is False:
        action, _, _ = self.policy.sample(state)
    else:
        _, _, action = self.policy.sample(state)
    return action.detach().cpu().numpy()[0]

def update_parameters(self, memory, batch_size, updates):
    # Sample a batch from memory
    state_batch, action_batch, reward_batch, cost_batch, next_state_batch, mask_batch = memory.sample(batch_size=batch_size)

    state_batch = torch.FloatTensor(state_batch).to(self.device)
    next_state_batch = torch.FloatTensor(next_state_batch).to(self.device)
    action_batch = torch.FloatTensor(action_batch).to(self.device)
    reward_batch = torch.FloatTensor(reward_batch).to(self.device).unsqueeze(1)
    cost_batch = torch.FloatTensor(cost_batch).to(self.device).unsqueeze(1)
    mask_batch = torch.FloatTensor(mask_batch).to(self.device).unsqueeze(1)

    with torch.no_grad():
        # calculating the target q
        next_state_action, next_state_log_pi, _ = self.policy.sample(next_state_batch)
        qf1_next_target, qf2_next_target = self.critic_target(next_state_batch, next_state_action)
        min_qf_next_target = torch.min(qf1_next_target, qf2_next_target) - self.alpha * next_state_log_pi
        next_q_value = reward_batch + mask_batch * self.gamma * (min_qf_next_target)

        # calculating the target q_cost
        qf1_next_target_cost, qf2_next_target_cost = self.critic_target_c(next_state_batch, next_state_action)
        min_qf_next_target_cost = torch.min(qf1_next_target_cost, qf2_next_target_cost) - self.alpha * next_state_log_pi
        next_q_value_cost = cost_batch + mask_batch * self.gamma * (min_qf_next_target_cost)

    # calculating q
    qf1, qf2 = self.critic(state_batch, action_batch)  # Two Q-functions to mitigate positive bias in the policy improvement step
    qf1_loss = F.mse_loss(qf1, next_q_value)  # JQ = 𝔼(st,at)~D[0.5(Q1(st,at) - r(st,at) - γ(𝔼st+1~p[V(st+1)]))^2]
    qf2_loss = F.mse_loss(qf2, next_q_value)  # JQ = 𝔼(st,at)~D[0.5(Q1(st,at) - r(st,at) - γ(𝔼st+1~p[V(st+1)]))^2]
    qf_loss = qf1_loss + qf2_loss
    self.critic_optim.zero_grad()
    qf_loss.backward()
    self.critic_optim.step()

    # calculating q_cost
    qf1_cost, qf2_cost = self.critic_c(state_batch, action_batch)  # Two Q-functions to mitigate positive bias in the policy improvement step
    qf1_loss_cost = F.mse_loss(qf1_cost, next_q_value_cost)  # JQ = 𝔼(st,at)~D[0.5(Q1(st,at) - r(st,at) - γ(𝔼st+1~p[V(st+1)]))^2]
    qf2_loss_cost = F.mse_loss(qf2_cost, next_q_value_cost)  # JQ = 𝔼(st,at)~D[0.5(Q1(st,at) - r(st,at) - γ(𝔼st+1~p[V(st+1)]))^2]
    qf_loss_cost = qf1_loss_cost + qf2_loss_cost
    self.critic_optim_c.zero_grad()
    qf_loss_cost.backward()
    self.critic_optim_c.step()

    # updating the policy gradient ascent
    pi, log_pi, _ = self.policy.sample(state_batch)
    qf1_pi, qf2_pi = self.critic(state_batch, pi)
    min_qf_pi = torch.min(qf1_pi, qf2_pi)
    inside_term = ((self.alpha * log_pi) - min_qf_pi).mean() # Jπ = 𝔼st∼D,εt∼N[α * logπ(f(εt;st)|st) − Q(st,f(εt;st))]

    # updating the cost gradient decent
    qf1_pi_cost, qf2_pi_cost = self.critic_c(state_batch, pi)
    min_qf_pi_cost = torch.min(qf1_pi_cost, qf2_pi_cost)
    penalty = self.lam * min_qf_pi_cost
    policy_loss = (inside_term + penalty).sum(dim=1).mean()

    self.policy_optim.zero_grad()
    policy_loss.backward()
    self.policy_optim.step()

    if updates % self.lab_update_interval == 0:
        qf1_pi_cost, qf2_pi_cost = self.critic_c(state_batch, pi)
        violation = torch.min(qf1_pi_cost, qf2_pi_cost) - self.cost_lim
        self.log_lam = torch.nn.functional.softplus(self.lam)
        lambda_loss = self.log_lam * violation.detach()
        lambda_loss = -lambda_loss.sum(dim=-1)
        lambda_loss.backward(torch.ones_like(lambda_loss))
        self.lam_optimiser.step()

    if self.automatic_entropy_tuning:
        alpha_loss = -(self.log_alpha * (log_pi + self.target_entropy).detach()).mean()

        self.alpha_optim.zero_grad()
        alpha_loss.backward()
        self.alpha_optim.step()

        self.alpha = self.log_alpha.exp()
        alpha_tlogs = self.alpha.clone() # For TensorboardX logs
    else:
        alpha_loss = torch.tensor(0.).to(self.device)
        alpha_tlogs = torch.tensor(self.alpha) # For TensorboardX logs

    if updates % self.target_update_interval == 0:
        soft_update(self.critic_target, self.critic, self.tau)

    return qf1_loss.item(), qf2_loss.item(), policy_loss.item(), alpha_loss.item(), alpha_tlogs.item()

# Save model parameters
def save_checkpoint(self, env_name, suffix="", ckpt_path=None):
    if not os.path.exists('checkpoints/'):
        os.makedirs('checkpoints/')
    if ckpt_path is None:
        ckpt_path = "checkpoints/sac_checkpoint_{}_{}".format(env_name, suffix)
    print('Saving models to {}'.format(ckpt_path))
    torch.save({'policy_state_dict': self.policy.state_dict(),
                'critic_state_dict': self.critic.state_dict(),
                'critic_target_state_dict': self.critic_target.state_dict(),
                'critic_optimizer_state_dict': self.critic_optim.state_dict(),
                'policy_optimizer_state_dict': self.policy_optim.state_dict()}, ckpt_path)

# Load model parameters
def load_checkpoint(self, ckpt_path, evaluate=False):
    print('Loading models from {}'.format(ckpt_path))
    if ckpt_path is not None:
        checkpoint = torch.load(ckpt_path)
        self.policy.load_state_dict(checkpoint['policy_state_dict'])
        self.critic.load_state_dict(checkpoint['critic_state_dict'])
        self.critic_target.load_state_dict(checkpoint['critic_target_state_dict'])
        self.critic_optim.load_state_dict(checkpoint['critic_optimizer_state_dict'])
        self.policy_optim.load_state_dict(checkpoint['policy_optimizer_state_dict'])

        if evaluate:
            self.policy.eval()
            self.critic.eval()
            self.critic_target.eval()
        else:
            self.policy.train()
            self.critic.train()
            self.critic_target.train()

` The result is :

[xueliu8617112]

Hello, Dr.Haydari. After fixing several error. I found it works. Here is the code:

`import os import torch import torch.nn.functional as F from torch.optim import Adam from utils import soft_update, hard_update from modelrul import GaussianPolicy, QNetwork, DeterministicPolicy import numpy as np

class SAC(object): def init(self, num_inputs, action_space, args, lambda_init = 1.):

    self.gamma = args.gamma
    self.tau = args.tau
    self.alpha = args.alpha

    self.policy_type = args.policy
    self.target_update_interval = args.target_update_interval
    self.lab_update_interval = 12
    self.automatic_entropy_tuning = args.automatic_entropy_tuning

    self.device = torch.device("cuda" if args.cuda else "cpu")

    self.critic = QNetwork(num_inputs, action_space.shape[0], args.hidden_size).to(device=self.device)
    self.critic_optim = Adam(self.critic.parameters(), lr=args.lr)
    self.critic_target = QNetwork(num_inputs, action_space.shape[0], args.hidden_size).to(device=self.device)
    hard_update(self.critic_target, self.critic)

    self.critic_c = QNetwork(num_inputs, action_space.shape[0], args.hidden_size).to(device=self.device)
    self.critic_optim_c = Adam(self.critic_c.parameters(), lr=args.lr)
    self.critic_target_c = QNetwork(num_inputs, action_space.shape[0], args.hidden_size).to(device=self.device)
    hard_update(self.critic_target_c, self.critic_c)

    self.lam = torch.tensor(lambda_init, requires_grad=True)
    self.lam_optimiser = torch.optim.Adam([self.lam], lr=3e-4)

    self.cost_lim = 1500

    if self.policy_type == "Gaussian":
        # Target Entropy = −dim(A) (e.g. , -6 for HalfCheetah-v2) as given in the paper
        if self.automatic_entropy_tuning is True:
            self.target_entropy = -torch.prod(torch.Tensor(action_space.shape).to(self.device)).item()
            self.log_alpha = torch.zeros(1, requires_grad=True, device=self.device)
            self.alpha_optim = Adam([self.log_alpha], lr=args.lr)

        self.policy = GaussianPolicy(num_inputs, action_space.shape[0], args.hidden_size, action_space).to(self.device)
        self.policy_optim = Adam(self.policy.parameters(), lr=args.lr)

    else:
        self.alpha = 0
        self.automatic_entropy_tuning = False
        self.policy = DeterministicPolicy(num_inputs, action_space.shape[0], args.hidden_size, action_space).to(self.device)
        self.policy_optim = Adam(self.policy.parameters(), lr=args.lr)

def select_action(self, state, previous_action, time_now, evaluate=False):
    state = torch.FloatTensor(state).to(self.device).unsqueeze(0)
    if evaluate is False:
        action, _, _ = self.policy.sample(state, previous_action, time_now)
    else:
        _, _, action = self.policy.sample(state, previous_action, time_now)
    return action.detach().cpu().numpy()[0]

def update_parameters(self, memory, batch_size, updates):
    # Sample a batch from memory
    state_batch, action_batch, reward_batch, cost_batch, next_state_batch, mask_batch, previous_action, time_now = memory.sample(batch_size=batch_size)

    state_batch = torch.FloatTensor(state_batch).to(self.device)
    next_state_batch = torch.FloatTensor(next_state_batch).to(self.device)
    action_batch = torch.FloatTensor(action_batch).to(self.device)
    reward_batch = torch.FloatTensor(reward_batch).to(self.device).unsqueeze(1)
    cost_batch = torch.FloatTensor(cost_batch).to(self.device).unsqueeze(1)
    mask_batch = torch.FloatTensor(mask_batch).to(self.device).unsqueeze(1)
    previous_action =  torch.FloatTensor(previous_action).to(self.device).squeeze(1)
    time_now = torch.FloatTensor(time_now).to(self.device)

    with torch.no_grad():
        # calculating the target q
        next_state_action, next_state_log_pi, _ = self.policy.sample(next_state_batch, action_batch, time_now)
        qf1_next_target, qf2_next_target = self.critic_target(next_state_batch, next_state_action)
        min_qf_next_target = torch.min(qf1_next_target, qf2_next_target) - self.alpha * next_state_log_pi
        next_q_value = reward_batch + mask_batch * self.gamma * (min_qf_next_target)

        # calculating the target q_cost
        qf1_next_target_cost, qf2_next_target_cost = self.critic_target_c(next_state_batch, next_state_action)
        min_qf_next_target_cost = torch.min(qf1_next_target_cost, qf2_next_target_cost)
        next_q_value_cost = cost_batch + mask_batch * self.gamma * (min_qf_next_target_cost)

    # calculating q
    qf1, qf2 = self.critic(state_batch, action_batch)  # Two Q-functions to mitigate positive bias in the policy improvement step
    qf1_loss = F.mse_loss(qf1, next_q_value)  # JQ = 𝔼(st,at)~D[0.5(Q1(st,at) - r(st,at) - γ(𝔼st+1~p[V(st+1)]))^2]
    qf2_loss = F.mse_loss(qf2, next_q_value)  # JQ = 𝔼(st,at)~D[0.5(Q1(st,at) - r(st,at) - γ(𝔼st+1~p[V(st+1)]))^2]
    qf_loss = qf1_loss + qf2_loss
    self.critic_optim.zero_grad()
    qf_loss.backward()
    self.critic_optim.step()

    # calculating q_cost
    qf1_cost, qf2_cost = self.critic_c(state_batch, action_batch)  # Two Q-functions to mitigate positive bias in the policy improvement step
    qf1_loss_cost = F.mse_loss(qf1_cost, next_q_value_cost)  # JQ = 𝔼(st,at)~D[0.5(Q1(st,at) - r(st,at) - γ(𝔼st+1~p[V(st+1)]))^2]
    qf2_loss_cost = F.mse_loss(qf2_cost, next_q_value_cost)  # JQ = 𝔼(st,at)~D[0.5(Q1(st,at) - r(st,at) - γ(𝔼st+1~p[V(st+1)]))^2]
    qf_loss_cost = qf1_loss_cost + qf2_loss_cost
    self.critic_optim_c.zero_grad()
    qf_loss_cost.backward()
    self.critic_optim_c.step()

    # updating the policy gradient ascent
    pi, log_pi, _ = self.policy.sample(state_batch, previous_action, time_now - 1)

    qf1_pi, qf2_pi = self.critic(state_batch, pi)
    min_qf_pi = torch.min(qf1_pi, qf2_pi)
    inside_term = ((self.alpha * log_pi) - min_qf_pi).mean()  # Jπ = 𝔼st∼D,εt∼N[α * logπ(f(εt;st)|st) − Q(st,f(εt;st))]

    # updating the cost gradient decent
    qf1_pi_cost, qf2_pi_cost = self.critic_c(state_batch, pi)
    min_qf_pi_cost = torch.min(qf1_pi_cost, qf2_pi_cost)
    penalty = self.lam * (min_qf_pi_cost - self.cost_lim)
    policy_loss = (inside_term + penalty).sum(dim=1).mean()

    self.policy_optim.zero_grad()
    policy_loss.backward()
    self.policy_optim.step()

    if updates % self.lab_update_interval == 0:
        violation = self.cost_lim - min_qf_pi_cost
        self.log_lam = torch.nn.functional.softplus(self.lam)
        lambda_loss = self.log_lam * violation.detach()
        lambda_loss = lambda_loss.sum()
        lambda_loss.backward(torch.ones_like(lambda_loss))
        self.lam_optimiser.step()

    if self.automatic_entropy_tuning:

        alpha_loss = -(self.log_alpha * (log_pi + self.target_entropy).detach()).mean()
        self.alpha_optim.zero_grad()
        alpha_loss.backward()
        self.alpha_optim.step()

        self.alpha = self.log_alpha.exp()
        alpha_tlogs = self.alpha.clone() # For TensorboardX logs
    else:
        alpha_loss = torch.tensor(0.).to(self.device)
        alpha_tlogs = torch.tensor(self.alpha) # For TensorboardX logs

    if updates % self.target_update_interval == 0:
        soft_update(self.critic_target, self.critic, self.tau)
        soft_update(self.critic_target_c, self.critic_c, self.tau)

    return qf1_loss.item(), qf2_loss.item(), qf1_loss_cost.item(), qf2_loss_cost.item(), policy_loss.item(), alpha_loss.item(), alpha_tlogs.item(), self.log_lam.item()

# Save model parameters
def save_checkpoint(self, env_name, suffix="", ckpt_path=None):
    if not os.path.exists('checkpoints/'):
        os.makedirs('checkpoints/')
    if ckpt_path is None:
        ckpt_path = "checkpoints/sac_checkpoint_{}_{}".format(env_name, suffix)
    print('Saving models to {}'.format(ckpt_path))
    torch.save({'policy_state_dict': self.policy.state_dict(),
                'critic_state_dict': self.critic.state_dict(),
                'critic_target_state_dict': self.critic_target.state_dict(),
                'critic_optimizer_state_dict': self.critic_optim.state_dict(),
                'policy_optimizer_state_dict': self.policy_optim.state_dict()}, ckpt_path)

# Load model parameters
def load_checkpoint(self, ckpt_path, evaluate=False):
    print('Loading models from {}'.format(ckpt_path))
    if ckpt_path is not None:
        checkpoint = torch.load(ckpt_path)
        self.policy.load_state_dict(checkpoint['policy_state_dict'])
        self.critic.load_state_dict(checkpoint['critic_state_dict'])
        self.critic_target.load_state_dict(checkpoint['critic_target_state_dict'])
        self.critic_optim.load_state_dict(checkpoint['critic_optimizer_state_dict'])
        self.policy_optim.load_state_dict(checkpoint['policy_optimizer_state_dict'])

        if evaluate:
            self.policy.eval()
            self.critic.eval()
            self.critic_target.eval()
            self.critic_c.eval()
            self.critic_target_c.eval()

        else:
            self.policy.train()
            self.critic.train()
            self.critic_target.train()
            self.critic_c.train()
            self.critic_target_c.train()

`

[XPStone]

@xueliu8617112 Dr.Liu，I noticed that you updated it using the ‘self.cost_lim - min_qf_pi_cost‘，I found that this is not conducive to convergence of cost in my experiments ,Do you think it is appropriate to take a min value for cost like reward?

[xueliu8617112]

@xueliu8617112 Dr.Liu，I noticed that you updated it using the ‘self.cost_lim - min_qf_pi_cost‘，I found that this is not conducive to convergence of cost in my experiments ,Do you think it is appropriate to take a min value for cost like reward?

The value of self.cost_lim depends on your env setting. U can try a min value.

[hanjiangfeibing]

Hello, Dr.Haydari. After fixing several error. I found it works. Here is the code:

`import os import torch import torch.nn.functional as F from torch.optim import Adam from utils import soft_update, hard_update from modelrul import GaussianPolicy, QNetwork, DeterministicPolicy import numpy as np

class SAC(object): def init(self, num_inputs, action_space, args, lambda_init = 1.):

    self.gamma = args.gamma
    self.tau = args.tau
    self.alpha = args.alpha

    self.policy_type = args.policy
    self.target_update_interval = args.target_update_interval
    self.lab_update_interval = 12
    self.automatic_entropy_tuning = args.automatic_entropy_tuning

    self.device = torch.device("cuda" if args.cuda else "cpu")

    self.critic = QNetwork(num_inputs, action_space.shape[0], args.hidden_size).to(device=self.device)
    self.critic_optim = Adam(self.critic.parameters(), lr=args.lr)
    self.critic_target = QNetwork(num_inputs, action_space.shape[0], args.hidden_size).to(device=self.device)
    hard_update(self.critic_target, self.critic)

    self.critic_c = QNetwork(num_inputs, action_space.shape[0], args.hidden_size).to(device=self.device)
    self.critic_optim_c = Adam(self.critic_c.parameters(), lr=args.lr)
    self.critic_target_c = QNetwork(num_inputs, action_space.shape[0], args.hidden_size).to(device=self.device)
    hard_update(self.critic_target_c, self.critic_c)

    self.lam = torch.tensor(lambda_init, requires_grad=True)
    self.lam_optimiser = torch.optim.Adam([self.lam], lr=3e-4)

    self.cost_lim = 1500

    if self.policy_type == "Gaussian":
        # Target Entropy = −dim(A) (e.g. , -6 for HalfCheetah-v2) as given in the paper
        if self.automatic_entropy_tuning is True:
            self.target_entropy = -torch.prod(torch.Tensor(action_space.shape).to(self.device)).item()
            self.log_alpha = torch.zeros(1, requires_grad=True, device=self.device)
            self.alpha_optim = Adam([self.log_alpha], lr=args.lr)

        self.policy = GaussianPolicy(num_inputs, action_space.shape[0], args.hidden_size, action_space).to(self.device)
        self.policy_optim = Adam(self.policy.parameters(), lr=args.lr)

    else:
        self.alpha = 0
        self.automatic_entropy_tuning = False
        self.policy = DeterministicPolicy(num_inputs, action_space.shape[0], args.hidden_size, action_space).to(self.device)
        self.policy_optim = Adam(self.policy.parameters(), lr=args.lr)

def select_action(self, state, previous_action, time_now, evaluate=False):
    state = torch.FloatTensor(state).to(self.device).unsqueeze(0)
    if evaluate is False:
        action, _, _ = self.policy.sample(state, previous_action, time_now)
    else:
        _, _, action = self.policy.sample(state, previous_action, time_now)
    return action.detach().cpu().numpy()[0]

def update_parameters(self, memory, batch_size, updates):
    # Sample a batch from memory
    state_batch, action_batch, reward_batch, cost_batch, next_state_batch, mask_batch, previous_action, time_now = memory.sample(batch_size=batch_size)

    state_batch = torch.FloatTensor(state_batch).to(self.device)
    next_state_batch = torch.FloatTensor(next_state_batch).to(self.device)
    action_batch = torch.FloatTensor(action_batch).to(self.device)
    reward_batch = torch.FloatTensor(reward_batch).to(self.device).unsqueeze(1)
    cost_batch = torch.FloatTensor(cost_batch).to(self.device).unsqueeze(1)
    mask_batch = torch.FloatTensor(mask_batch).to(self.device).unsqueeze(1)
    previous_action =  torch.FloatTensor(previous_action).to(self.device).squeeze(1)
    time_now = torch.FloatTensor(time_now).to(self.device)

    with torch.no_grad():
        # calculating the target q
        next_state_action, next_state_log_pi, _ = self.policy.sample(next_state_batch, action_batch, time_now)
        qf1_next_target, qf2_next_target = self.critic_target(next_state_batch, next_state_action)
        min_qf_next_target = torch.min(qf1_next_target, qf2_next_target) - self.alpha * next_state_log_pi
        next_q_value = reward_batch + mask_batch * self.gamma * (min_qf_next_target)

        # calculating the target q_cost
        qf1_next_target_cost, qf2_next_target_cost = self.critic_target_c(next_state_batch, next_state_action)
        min_qf_next_target_cost = torch.min(qf1_next_target_cost, qf2_next_target_cost)
        next_q_value_cost = cost_batch + mask_batch * self.gamma * (min_qf_next_target_cost)

    # calculating q
    qf1, qf2 = self.critic(state_batch, action_batch)  # Two Q-functions to mitigate positive bias in the policy improvement step
    qf1_loss = F.mse_loss(qf1, next_q_value)  # JQ = 𝔼(st,at)~D[0.5(Q1(st,at) - r(st,at) - γ(𝔼st+1~p[V(st+1)]))^2]
    qf2_loss = F.mse_loss(qf2, next_q_value)  # JQ = 𝔼(st,at)~D[0.5(Q1(st,at) - r(st,at) - γ(𝔼st+1~p[V(st+1)]))^2]
    qf_loss = qf1_loss + qf2_loss
    self.critic_optim.zero_grad()
    qf_loss.backward()
    self.critic_optim.step()

    # calculating q_cost
    qf1_cost, qf2_cost = self.critic_c(state_batch, action_batch)  # Two Q-functions to mitigate positive bias in the policy improvement step
    qf1_loss_cost = F.mse_loss(qf1_cost, next_q_value_cost)  # JQ = 𝔼(st,at)~D[0.5(Q1(st,at) - r(st,at) - γ(𝔼st+1~p[V(st+1)]))^2]
    qf2_loss_cost = F.mse_loss(qf2_cost, next_q_value_cost)  # JQ = 𝔼(st,at)~D[0.5(Q1(st,at) - r(st,at) - γ(𝔼st+1~p[V(st+1)]))^2]
    qf_loss_cost = qf1_loss_cost + qf2_loss_cost
    self.critic_optim_c.zero_grad()
    qf_loss_cost.backward()
    self.critic_optim_c.step()

    # updating the policy gradient ascent
    pi, log_pi, _ = self.policy.sample(state_batch, previous_action, time_now - 1)

    qf1_pi, qf2_pi = self.critic(state_batch, pi)
    min_qf_pi = torch.min(qf1_pi, qf2_pi)
    inside_term = ((self.alpha * log_pi) - min_qf_pi).mean()  # Jπ = 𝔼st∼D,εt∼N[α * logπ(f(εt;st)|st) − Q(st,f(εt;st))]

    # updating the cost gradient decent
    qf1_pi_cost, qf2_pi_cost = self.critic_c(state_batch, pi)
    min_qf_pi_cost = torch.min(qf1_pi_cost, qf2_pi_cost)
    penalty = self.lam * (min_qf_pi_cost - self.cost_lim)
    policy_loss = (inside_term + penalty).sum(dim=1).mean()

    self.policy_optim.zero_grad()
    policy_loss.backward()
    self.policy_optim.step()

    if updates % self.lab_update_interval == 0:
        violation = self.cost_lim - min_qf_pi_cost
        self.log_lam = torch.nn.functional.softplus(self.lam)
        lambda_loss = self.log_lam * violation.detach()
        lambda_loss = lambda_loss.sum()
        lambda_loss.backward(torch.ones_like(lambda_loss))
        self.lam_optimiser.step()

    if self.automatic_entropy_tuning:

        alpha_loss = -(self.log_alpha * (log_pi + self.target_entropy).detach()).mean()
        self.alpha_optim.zero_grad()
        alpha_loss.backward()
        self.alpha_optim.step()

        self.alpha = self.log_alpha.exp()
        alpha_tlogs = self.alpha.clone() # For TensorboardX logs
    else:
        alpha_loss = torch.tensor(0.).to(self.device)
        alpha_tlogs = torch.tensor(self.alpha) # For TensorboardX logs

    if updates % self.target_update_interval == 0:
        soft_update(self.critic_target, self.critic, self.tau)
        soft_update(self.critic_target_c, self.critic_c, self.tau)

    return qf1_loss.item(), qf2_loss.item(), qf1_loss_cost.item(), qf2_loss_cost.item(), policy_loss.item(), alpha_loss.item(), alpha_tlogs.item(), self.log_lam.item()

# Save model parameters
def save_checkpoint(self, env_name, suffix="", ckpt_path=None):
    if not os.path.exists('checkpoints/'):
        os.makedirs('checkpoints/')
    if ckpt_path is None:
        ckpt_path = "checkpoints/sac_checkpoint_{}_{}".format(env_name, suffix)
    print('Saving models to {}'.format(ckpt_path))
    torch.save({'policy_state_dict': self.policy.state_dict(),
                'critic_state_dict': self.critic.state_dict(),
                'critic_target_state_dict': self.critic_target.state_dict(),
                'critic_optimizer_state_dict': self.critic_optim.state_dict(),
                'policy_optimizer_state_dict': self.policy_optim.state_dict()}, ckpt_path)

# Load model parameters
def load_checkpoint(self, ckpt_path, evaluate=False):
    print('Loading models from {}'.format(ckpt_path))
    if ckpt_path is not None:
        checkpoint = torch.load(ckpt_path)
        self.policy.load_state_dict(checkpoint['policy_state_dict'])
        self.critic.load_state_dict(checkpoint['critic_state_dict'])
        self.critic_target.load_state_dict(checkpoint['critic_target_state_dict'])
        self.critic_optim.load_state_dict(checkpoint['critic_optimizer_state_dict'])
        self.policy_optim.load_state_dict(checkpoint['policy_optimizer_state_dict'])

        if evaluate:
            self.policy.eval()
            self.critic.eval()
            self.critic_target.eval()
            self.critic_c.eval()
            self.critic_target_c.eval()

        else:
            self.policy.train()
            self.critic.train()
            self.critic_target.train()
            self.critic_c.train()
            self.critic_target_c.train()

` Dr. Liu, I have the same problem as you. I can't understand why adding actions and time to the actor can solve the problem with your modified code?