diff --git a/MuJoCo/ppo.py b/MuJoCo/ppo.py
new file mode 100644
index 0000000..360ae5f
--- /dev/null
+++ b/MuJoCo/ppo.py
@@ -0,0 +1,311 @@
+import argparse
+import os
+import random
+import time
+
+import gymnasium as gym
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.distributions.normal import Normal
+from torch.utils.tensorboard import SummaryWriter
+from tqdm import tqdm
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--exp_name', type=str, default=os.path.basename(__file__).rstrip('.py'))
+    parser.add_argument('--seed', type=int, default=1)
+    parser.add_argument('--torch_deterministic', type=bool, default=True)
+    parser.add_argument('--cuda', type=bool, default=True)
+    parser.add_argument('--env_id', type=str, default='Humanoid-v4')
+    parser.add_argument('--total_time_steps', type=int, default=int(1e7))
+    parser.add_argument('--learning_rate', type=float, default=3e-4)
+    parser.add_argument('--num_envs', type=int, default=8)
+    parser.add_argument('--num_steps', type=int, default=256)
+    parser.add_argument('--anneal_lr', type=bool, default=True)
+    parser.add_argument('--gamma', type=float, default=0.99)
+    parser.add_argument('--gae_lambda', type=float, default=0.95)
+    parser.add_argument('--num_mini_batches', type=int, default=4)
+    parser.add_argument('--update_epochs', type=int, default=10)
+    parser.add_argument('--norm_adv', type=bool, default=True)
+    parser.add_argument('--clip_value_loss', type=bool, default=True)
+    parser.add_argument('--c_1', type=float, default=0.5)
+    parser.add_argument('--c_2', type=float, default=0.0)
+    parser.add_argument('--max_grad_norm', type=float, default=0.5)
+    parser.add_argument('--clip_epsilon', type=float, default=0.2)
+    a = parser.parse_args()
+    a.batch_size = int(a.num_envs * a.num_steps)
+    a.minibatch_size = int(a.batch_size // a.num_mini_batches)
+    return a
+
+
+def make_env(env_id, gamma):
+    def thunk():
+        env = gym.make(env_id)
+        env = gym.wrappers.FlattenObservation(env)
+        env = gym.wrappers.RecordEpisodeStatistics(env)
+        env = gym.wrappers.ClipAction(env)
+        env = gym.wrappers.NormalizeObservation(env)
+        env = gym.wrappers.TransformObservation(env, lambda o: np.clip(o, -10, 10))
+        env = gym.wrappers.NormalizeReward(env, gamma=gamma)
+        env = gym.wrappers.TransformReward(env, lambda r: float(np.clip(r, -10, 10)))
+        return env
+    return thunk
+
+
+def layer_init(layer, s=np.sqrt(2), bias_const=0.0):
+    torch.nn.init.orthogonal_(layer.weight, s)
+    torch.nn.init.constant_(layer.bias, bias_const)
+    return layer
+
+
+class Agent(nn.Module):
+    def __init__(self, e):
+        super().__init__()
+        self.critic = nn.Sequential(
+            layer_init(nn.Linear(np.array(e.single_observation_space.shape).prod(), 64)),
+            nn.Tanh(),
+            layer_init(nn.Linear(64, 64)),
+            nn.Tanh(),
+            layer_init(nn.Linear(64, 1), s=1.0),
+        )
+        self.actor_mean = nn.Sequential(
+            layer_init(nn.Linear(np.array(e.single_observation_space.shape).prod(), 64)),
+            nn.Tanh(),
+            layer_init(nn.Linear(64, 64)),
+            nn.Tanh(),
+            layer_init(nn.Linear(64, np.array(e.single_action_space.shape).prod()), s=0.01),
+        )
+        self.actor_log_std = nn.Parameter(torch.zeros(1, np.array(e.single_action_space.shape).prod()))
+
+    def get_value(self, x):
+        return self.critic(x)
+
+    def get_action_and_value(self, x, a=None, show_all=False):
+        action_mean = self.actor_mean(x)
+        action_log_std = self.actor_log_std.expand_as(action_mean)
+        action_std = torch.exp(action_log_std)
+        probs = Normal(action_mean, action_std)
+        if a is None:
+            a = probs.sample()
+        if show_all:
+            return a, probs.log_prob(a).sum(1), probs.entropy().sum(1), self.critic(x), probs
+        return a, probs.log_prob(a).sum(1), probs.entropy().sum(1), self.critic(x)
+
+
+def compute_kld(mu_1, sigma_1, mu_2, sigma_2):
+    return torch.log(sigma_2 / sigma_1) + ((mu_1 - mu_2) ** 2 + (sigma_1 ** 2 - sigma_2 ** 2)) / (2 * sigma_2 ** 2)
+
+
+def main(env_id, seed):
+    args = get_args()
+    args.env_id = env_id
+    args.seed = seed
+    run_name = (
+            'ppo' +
+            '_epoch_' + str(args.update_epochs) +
+            '_seed_' + str(args.seed)
+    )
+
+    # Save training logs
+    path_string = str(args.env_id) + '/' + run_name
+    writer = SummaryWriter(path_string)
+    writer.add_text(
+        'Hyperparameter',
+        '|param|value|\n|-|-|\n%s' % ('\n'.join([f'|{key}|{value}|' for key, value in vars(args).items()])),
+    )
+
+    # Random seed
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    torch.backends.cudnn.deterministic = args.torch_deterministic
+
+    device = torch.device('cuda' if torch.cuda.is_available() and args.cuda else 'cpu')
+
+    # Initialize environments
+    envs = gym.vector.SyncVectorEnv(
+        [make_env(args.env_id, args.gamma) for _ in range(args.num_envs)]
+    )
+    assert isinstance(envs.single_action_space, gym.spaces.Box), 'only continuous action space is supported'
+
+    agent = Agent(envs).to(device)
+    optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5)
+
+    # Initialize buffer
+    obs = torch.zeros((args.num_steps, args.num_envs) + envs.single_observation_space.shape).to(device)
+    actions = torch.zeros((args.num_steps, args.num_envs) + envs.single_action_space.shape).to(device)
+    log_probs = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    rewards = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    dones = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    values = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    mean = torch.zeros((args.num_steps, args.num_envs) + envs.single_action_space.shape).to(device)
+    std = torch.zeros((args.num_steps, args.num_envs) + envs.single_action_space.shape).to(device)
+
+    # Data collection
+    global_step = 0
+    start_time = time.time()
+    next_obs, _ = envs.reset(seed=args.seed)
+    next_obs = torch.Tensor(next_obs).to(device)
+    next_done = torch.zeros(args.num_envs).to(device)
+    num_updates = args.total_time_steps // args.batch_size
+
+    for update in tqdm(range(1, num_updates + 1)):
+
+        # Linear decay of learning rate
+        if args.anneal_lr:
+            frac = 1.0 - (update - 1.0) / num_updates
+            lr_now = frac * args.learning_rate
+            optimizer.param_groups[0]['lr'] = lr_now
+
+        for step in range(0, args.num_steps):
+            global_step += 1 * args.num_envs
+            obs[step] = next_obs
+            dones[step] = next_done
+
+            # Compute the logarithm of the action probability output by the old policy network
+            with torch.no_grad():
+                action, log_prob, _, value, mean_std = agent.get_action_and_value(next_obs, show_all=True)
+                values[step] = value.flatten()
+            actions[step] = action
+            log_probs[step] = log_prob
+
+            # Mean and standard deviation (mini_batch_size, num_envs, action_dim)
+            mean[step] = mean_std.loc
+            std[step] = mean_std.scale
+
+            # Update the environments
+            next_obs, reward, terminations, truncations, info = envs.step(action.cpu().numpy())
+            done = np.logical_or(terminations, truncations)
+            rewards[step] = torch.tensor(reward).to(device).view(-1)
+            next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(done).to(device)
+
+            if 'final_info' not in info:
+                continue
+
+            for item in info['final_info']:
+                if item is None:
+                    continue
+                writer.add_scalar('charts/episodic_return', item['episode']['r'][0], global_step)
+
+        # Use GAE (Generalized Advantage Estimation) technique to estimate the advantage function
+        with torch.no_grad():
+            next_value = agent.get_value(next_obs).reshape(1, -1)
+            advantages = torch.zeros_like(rewards).to(device)
+            last_gae_lam = 0
+            for t in reversed(range(args.num_steps)):
+                if t == args.num_steps - 1:
+                    next_non_terminal = 1.0 - next_done
+                    next_values = next_value
+                else:
+                    next_non_terminal = 1.0 - dones[t + 1]
+                    next_values = values[t + 1]
+                delta = rewards[t] + args.gamma * next_values * next_non_terminal - values[t]
+                advantages[t] = last_gae_lam = delta + args.gamma * args.gae_lambda * next_non_terminal * last_gae_lam
+            returns = advantages + values
+
+        # ---------------------- We have collected enough data, now let's start training ---------------------- #
+        # Flatten each batch
+        b_obs = obs.reshape((-1,) + envs.single_observation_space.shape)
+        b_log_probs = log_probs.reshape(-1)
+        b_actions = actions.reshape((-1,) + envs.single_action_space.shape)
+        b_advantages = advantages.reshape(-1)
+        b_returns = returns.reshape(-1)
+        b_values = values.reshape(-1)
+
+        # Obtain the mean and the standard deviation of a batch
+        b_mean = mean.reshape(args.batch_size, -1)
+        b_std = std.reshape(args.batch_size, -1)
+
+        # Update the policy network and value network
+        b_index = np.arange(args.batch_size)
+        for epoch in range(1, args.update_epochs + 1):
+            np.random.shuffle(b_index)
+            t = 0
+            for start in range(0, args.batch_size, args.minibatch_size):
+                t += 1
+                end = start + args.minibatch_size
+                mb_index = b_index[start:end]
+
+                # The latest outputs of the policy network and value network
+                _, new_log_prob, entropy, new_value, new_mean_std = agent.get_action_and_value(b_obs[mb_index],
+                                                                                               b_actions[mb_index],
+                                                                                               show_all=True)
+                # Compute KL divergence
+                new_mean = new_mean_std.loc.reshape(args.minibatch_size, -1)
+                new_std = new_mean_std.scale.reshape(args.minibatch_size, -1)
+                d = compute_kld(b_mean[mb_index], b_std[mb_index], new_mean, new_std).sum(1)
+
+                writer.add_scalar('charts/average_kld', d.mean(), global_step)
+                writer.add_scalar('others/min_kld', d.min(), global_step)
+                writer.add_scalar('others/max_kld', d.max(), global_step)
+
+                log_ratio = new_log_prob - b_log_probs[mb_index]
+                ratios = log_ratio.exp()
+                mb_advantages = b_advantages[mb_index]
+
+                # Advantage normalization
+                if args.norm_adv:
+                    mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-12)
+
+                # Policy loss
+                pg_loss1 = -mb_advantages * ratios
+                pg_loss2 = -mb_advantages * torch.clamp(ratios, 1 - args.clip_epsilon, 1 + args.clip_epsilon)
+                pg_loss = torch.max(pg_loss1, pg_loss2).mean()
+
+                # Value loss
+                new_value = new_value.view(-1)
+                if args.clip_value_loss:
+                    v_loss_un_clipped = (new_value - b_returns[mb_index]) ** 2
+                    v_clipped = b_values[mb_index] + torch.clamp(
+                        new_value - b_values[mb_index],
+                        -args.clip_epsilon,
+                        args.clip_epsilon,
+                    )
+                    v_loss_clipped = (v_clipped - b_returns[mb_index]) ** 2
+                    v_loss_max = torch.max(v_loss_un_clipped, v_loss_clipped)
+                    v_loss = 0.5 * v_loss_max.mean()
+                else:
+                    v_loss = 0.5 * ((new_value - b_returns[mb_index]) ** 2).mean()
+
+                # Policy entropy
+                entropy_loss = entropy.mean()
+
+                # Total loss
+                loss = pg_loss + v_loss * args.c_1 - entropy_loss * args.c_2
+
+                # Save the data during the training process
+                writer.add_scalar('losses/policy_loss', pg_loss.item(), global_step)
+                writer.add_scalar('losses/value_loss', v_loss.item(), global_step)
+                writer.add_scalar('losses/entropy', entropy_loss.item(), global_step)
+
+                # Update network parameters
+                optimizer.zero_grad()
+                loss.backward()
+                nn.utils.clip_grad_norm_(agent.parameters(), args.max_grad_norm)
+                optimizer.step()
+
+        y_pre, y_true = b_values.cpu().numpy(), b_returns.cpu().numpy()
+        var_y = np.var(y_true)
+        explained_var = np.nan if var_y == 0 else 1 - np.var(y_true - y_pre) / var_y
+        writer.add_scalar('others/explained_variance', explained_var, global_step)
+
+        # Save the data during the training process
+        writer.add_scalar('charts/learning_rate', optimizer.param_groups[0]['lr'], global_step)
+        writer.add_scalar('charts/SPS', int(global_step / (time.time() - start_time)), global_step)
+
+    envs.close()
+    writer.close()
+
+
+def run():
+    for env_id in ['Humanoid-v4']:
+        for seed in range(1, 6):
+            print(env_id, 'seed:', seed)
+            main(env_id, seed)
+
+
+if __name__ == '__main__':
+    run()
diff --git a/MuJoCo/spo.py b/MuJoCo/spo.py
new file mode 100644
index 0000000..ab2160e
--- /dev/null
+++ b/MuJoCo/spo.py
@@ -0,0 +1,320 @@
+import argparse
+import os
+import random
+import time
+
+import gymnasium as gym
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.distributions.normal import Normal
+from torch.utils.tensorboard import SummaryWriter
+from tqdm import tqdm
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--exp_name', type=str, default=os.path.basename(__file__).rstrip('.py'))
+    parser.add_argument('--seed', type=int, default=1)
+    parser.add_argument('--torch_deterministic', type=bool, default=True)
+    parser.add_argument('--cuda', type=bool, default=True)
+    parser.add_argument('--env_id', type=str, default='Humanoid-v4')
+    parser.add_argument('--total_time_steps', type=int, default=int(1e7))
+    parser.add_argument('--learning_rate', type=float, default=3e-4)
+    parser.add_argument('--num_envs', type=int, default=8)
+    parser.add_argument('--num_steps', type=int, default=256)
+    parser.add_argument('--anneal_lr', type=bool, default=True)
+    parser.add_argument('--gamma', type=float, default=0.99)
+    parser.add_argument('--gae_lambda', type=float, default=0.95)
+    parser.add_argument('--num_mini_batches', type=int, default=4)
+    parser.add_argument('--update_epochs', type=int, default=10)
+    parser.add_argument('--norm_adv', type=bool, default=True)
+    parser.add_argument('--clip_value_loss', type=bool, default=True)
+    parser.add_argument('--c_1', type=float, default=0.5)
+    parser.add_argument('--c_2', type=float, default=0.0)
+    parser.add_argument('--max_grad_norm', type=float, default=0.5)
+    parser.add_argument('--kld_max', type=float, default=0.02)
+    a = parser.parse_args()
+    a.batch_size = int(a.num_envs * a.num_steps)
+    a.minibatch_size = int(a.batch_size // a.num_mini_batches)
+    return a
+
+
+def make_env(env_id, gamma):
+    def thunk():
+        env = gym.make(env_id)
+        env = gym.wrappers.FlattenObservation(env)
+        env = gym.wrappers.RecordEpisodeStatistics(env)
+        env = gym.wrappers.ClipAction(env)
+        env = gym.wrappers.NormalizeObservation(env)
+        env = gym.wrappers.TransformObservation(env, lambda o: np.clip(o, -10, 10))
+        env = gym.wrappers.NormalizeReward(env, gamma=gamma)
+        env = gym.wrappers.TransformReward(env, lambda r: float(np.clip(r, -10, 10)))
+        return env
+    return thunk
+
+
+def layer_init(layer, s=np.sqrt(2), bias_const=0.0):
+    torch.nn.init.orthogonal_(layer.weight, s)
+    torch.nn.init.constant_(layer.bias, bias_const)
+    return layer
+
+
+class Agent(nn.Module):
+    def __init__(self, e):
+        super().__init__()
+        self.critic = nn.Sequential(
+            layer_init(nn.Linear(np.array(e.single_observation_space.shape).prod(), 64)),
+            nn.Tanh(),
+            layer_init(nn.Linear(64, 64)),
+            nn.Tanh(),
+            layer_init(nn.Linear(64, 1), s=1.0),
+        )
+        self.actor_mean = nn.Sequential(
+            layer_init(nn.Linear(np.array(e.single_observation_space.shape).prod(), 64)),
+            nn.Tanh(),
+            layer_init(nn.Linear(64, 64)),
+            nn.Tanh(),
+            layer_init(nn.Linear(64, np.array(e.single_action_space.shape).prod()), s=0.01),
+        )
+        self.actor_log_std = nn.Parameter(torch.zeros(1, np.array(e.single_action_space.shape).prod()))
+
+    def get_value(self, x):
+        return self.critic(x)
+
+    def get_action_and_value(self, x, a=None, show_all=False):
+        action_mean = self.actor_mean(x)
+        action_log_std = self.actor_log_std.expand_as(action_mean)
+        action_std = torch.exp(action_log_std)
+        probs = Normal(action_mean, action_std)
+        if a is None:
+            a = probs.sample()
+        if show_all:
+            return a, probs.log_prob(a).sum(1), probs.entropy().sum(1), self.critic(x), probs
+        return a, probs.log_prob(a).sum(1), probs.entropy().sum(1), self.critic(x)
+
+
+def compute_kld(mu_1, sigma_1, mu_2, sigma_2):
+    return torch.log(sigma_2 / sigma_1) + ((mu_1 - mu_2) ** 2 + (sigma_1 ** 2 - sigma_2 ** 2)) / (2 * sigma_2 ** 2)
+
+
+def main(env_id, seed):
+    args = get_args()
+    args.env_id = env_id
+    args.seed = seed
+    run_name = (
+            'spo' +
+            '_epoch_' + str(args.update_epochs) +
+            '_seed_' + str(args.seed)
+    )
+
+    # Save training logs
+    path_string = str(args.env_id) + '/' + run_name
+    writer = SummaryWriter(path_string)
+    writer.add_text(
+        'Hyperparameter',
+        '|param|value|\n|-|-|\n%s' % ('\n'.join([f'|{key}|{value}|' for key, value in vars(args).items()])),
+    )
+
+    # Random seed
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    torch.backends.cudnn.deterministic = args.torch_deterministic
+
+    device = torch.device('cuda' if torch.cuda.is_available() and args.cuda else 'cpu')
+
+    # Initialize environments
+    envs = gym.vector.SyncVectorEnv(
+        [make_env(args.env_id, args.gamma) for _ in range(args.num_envs)]
+    )
+    assert isinstance(envs.single_action_space, gym.spaces.Box), 'only continuous action space is supported'
+
+    agent = Agent(envs).to(device)
+    optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5)
+
+    # Initialize buffer
+    obs = torch.zeros((args.num_steps, args.num_envs) + envs.single_observation_space.shape).to(device)
+    actions = torch.zeros((args.num_steps, args.num_envs) + envs.single_action_space.shape).to(device)
+    log_probs = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    rewards = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    dones = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    values = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    mean = torch.zeros((args.num_steps, args.num_envs) + envs.single_action_space.shape).to(device)
+    std = torch.zeros((args.num_steps, args.num_envs) + envs.single_action_space.shape).to(device)
+
+    # Data collection
+    global_step = 0
+    start_time = time.time()
+    next_obs, _ = envs.reset(seed=args.seed)
+    next_obs = torch.Tensor(next_obs).to(device)
+    next_done = torch.zeros(args.num_envs).to(device)
+    num_updates = args.total_time_steps // args.batch_size
+
+    for update in tqdm(range(1, num_updates + 1)):
+
+        # Linear decay of learning rate
+        if args.anneal_lr:
+            frac = 1.0 - (update - 1.0) / num_updates
+            lr_now = frac * args.learning_rate
+            optimizer.param_groups[0]['lr'] = lr_now
+
+        for step in range(0, args.num_steps):
+            global_step += 1 * args.num_envs
+            obs[step] = next_obs
+            dones[step] = next_done
+
+            # Compute the logarithm of the action probability output by the old policy network
+            with torch.no_grad():
+                action, log_prob, _, value, mean_std = agent.get_action_and_value(next_obs, show_all=True)
+                values[step] = value.flatten()
+            actions[step] = action
+            log_probs[step] = log_prob
+
+            # Mean and standard deviation (mini_batch_size, num_envs, action_dim)
+            mean[step] = mean_std.loc
+            std[step] = mean_std.scale
+
+            # Update the environments
+            next_obs, reward, terminations, truncations, info = envs.step(action.cpu().numpy())
+            done = np.logical_or(terminations, truncations)
+            rewards[step] = torch.tensor(reward).to(device).view(-1)
+            next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(done).to(device)
+
+            if 'final_info' not in info:
+                continue
+
+            for item in info['final_info']:
+                if item is None:
+                    continue
+                writer.add_scalar('charts/episodic_return', item['episode']['r'][0], global_step)
+
+        # Use GAE (Generalized Advantage Estimation) technique to estimate the advantage function
+        with torch.no_grad():
+            next_value = agent.get_value(next_obs).reshape(1, -1)
+            advantages = torch.zeros_like(rewards).to(device)
+            last_gae_lam = 0
+            for t in reversed(range(args.num_steps)):
+                if t == args.num_steps - 1:
+                    next_non_terminal = 1.0 - next_done
+                    next_values = next_value
+                else:
+                    next_non_terminal = 1.0 - dones[t + 1]
+                    next_values = values[t + 1]
+                delta = rewards[t] + args.gamma * next_values * next_non_terminal - values[t]
+                advantages[t] = last_gae_lam = delta + args.gamma * args.gae_lambda * next_non_terminal * last_gae_lam
+            returns = advantages + values
+
+        # ---------------------- We have collected enough data, now let's start training ---------------------- #
+        # Flatten each batch
+        b_obs = obs.reshape((-1,) + envs.single_observation_space.shape)
+        b_log_probs = log_probs.reshape(-1)
+        b_actions = actions.reshape((-1,) + envs.single_action_space.shape)
+        b_advantages = advantages.reshape(-1)
+        b_returns = returns.reshape(-1)
+        b_values = values.reshape(-1)
+
+        # Obtain the mean and the standard deviation of a batch
+        b_mean = mean.reshape(args.batch_size, -1)
+        b_std = std.reshape(args.batch_size, -1)
+
+        # Update the policy network and value network
+        b_index = np.arange(args.batch_size)
+        for epoch in range(1, args.update_epochs + 1):
+            np.random.shuffle(b_index)
+            t = 0
+            for start in range(0, args.batch_size, args.minibatch_size):
+                t += 1
+                end = start + args.minibatch_size
+                mb_index = b_index[start:end]
+
+                # The latest outputs of the policy network and value network
+                _, new_log_prob, entropy, new_value, new_mean_std = agent.get_action_and_value(b_obs[mb_index],
+                                                                                               b_actions[mb_index],
+                                                                                               show_all=True)
+                # Compute KL divergence
+                new_mean = new_mean_std.loc.reshape(args.minibatch_size, -1)
+                new_std = new_mean_std.scale.reshape(args.minibatch_size, -1)
+                d = compute_kld(b_mean[mb_index], b_std[mb_index], new_mean, new_std).sum(1)
+
+                writer.add_scalar('charts/average_kld', d.mean(), global_step)
+                writer.add_scalar('others/min_kld', d.min(), global_step)
+                writer.add_scalar('others/max_kld', d.max(), global_step)
+
+                log_ratio = new_log_prob - b_log_probs[mb_index]
+                ratios = log_ratio.exp()
+                mb_advantages = b_advantages[mb_index]
+
+                # Advantage normalization
+                if args.norm_adv:
+                    mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-12)
+
+                # Policy loss (main code of SPO)
+                if epoch == 1 and t == 1:
+                    pg_loss = (-mb_advantages * ratios).mean()
+                else:
+                    # d_clip
+                    d_clip = torch.clamp(input=d, min=0, max=args.kld_max)
+                    # d_clip / d
+                    ratio = d_clip / (d + 1e-12)
+                    # sign_a
+                    sign_a = torch.sign(mb_advantages)
+                    # (d_clip / d + sign_a - 1) * sign_a
+                    result = (ratio + sign_a - 1) * sign_a
+                    pg_loss = (-mb_advantages * ratios * result).mean()
+
+                # Value loss
+                new_value = new_value.view(-1)
+                if args.clip_value_loss:
+                    v_loss_un_clipped = (new_value - b_returns[mb_index]) ** 2
+                    v_clipped = b_values[mb_index] + torch.clamp(
+                        new_value - b_values[mb_index],
+                        -0.2,
+                        0.2,
+                    )
+                    v_loss_clipped = (v_clipped - b_returns[mb_index]) ** 2
+                    v_loss_max = torch.max(v_loss_un_clipped, v_loss_clipped)
+                    v_loss = 0.5 * v_loss_max.mean()
+                else:
+                    v_loss = 0.5 * ((new_value - b_returns[mb_index]) ** 2).mean()
+
+                # Policy entropy
+                entropy_loss = entropy.mean()
+
+                # Total loss
+                loss = pg_loss + v_loss * args.c_1 - entropy_loss * args.c_2
+
+                # Save the data during the training process
+                writer.add_scalar('losses/policy_loss', pg_loss.item(), global_step)
+                writer.add_scalar('losses/value_loss', v_loss.item(), global_step)
+                writer.add_scalar('losses/entropy', entropy_loss.item(), global_step)
+
+                # Update network parameters
+                optimizer.zero_grad()
+                loss.backward()
+                nn.utils.clip_grad_norm_(agent.parameters(), args.max_grad_norm)
+                optimizer.step()
+
+        y_pre, y_true = b_values.cpu().numpy(), b_returns.cpu().numpy()
+        var_y = np.var(y_true)
+        explained_var = np.nan if var_y == 0 else 1 - np.var(y_true - y_pre) / var_y
+        writer.add_scalar('others/explained_variance', explained_var, global_step)
+
+        # Save the data during the training process
+        writer.add_scalar('charts/learning_rate', optimizer.param_groups[0]['lr'], global_step)
+        writer.add_scalar('charts/SPS', int(global_step / (time.time() - start_time)), global_step)
+
+    envs.close()
+    writer.close()
+
+
+def run():
+    for env_id in ['Humanoid-v4']:
+        for seed in range(1, 6):
+            print(env_id, 'seed:', seed)
+            main(env_id, seed)
+
+
+if __name__ == '__main__':
+    run()