diff --git a/README.md b/README.md
index 2b9396bad..7b7bdab87 100644
--- a/README.md
+++ b/README.md
@@ -128,8 +128,11 @@ OmniSafe requires Python 3.8+ and PyTorch 1.10+.
 ### Install from source
 
 ```bash
+# Clone the repo
 git clone https://github.com/PKU-MARL/omnisafe
 cd omnisafe
+
+# Create a conda environment
 conda create -n omnisafe python=3.8
 conda activate omnisafe
 
@@ -141,7 +144,7 @@ pip install -e .
 
 ```bash
 cd examples
-python train_policy.py --env-id SafetyPointGoal1-v0 --algo PPOLag --parallel 1
+python train_policy.py --algo PPOLag --env-id SafetyPointGoal1-v0 --parallel 1 --total-steps 1024000 --device cpu --vector-env-nums 1 --torch-threads 1
 ```
 
 **algo:**
@@ -208,29 +211,36 @@ More information about environments, please refer to [Safety Gymnasium](https://
 ```python
 import omnisafe
 
-env = 'SafetyPointGoal1-v0'
 
-agent = omnisafe.Agent('PPOLag', env)
+env_id = 'SafetyPointGoal1-v0'
+custom_cfgs = {
+    'train_cfgs': {
+        'total_steps': 1024000,
+        'vector_env_nums': 1,
+        '--parallel': 1,
+    },
+    'algo_cfgs': {
+        'update_cycle': 2048,
+        'update_iters': 1,
+    },
+    'logger_cfgs': {
+        'use_wandb': False,
+    },
+}
+
+agent = omnisafe.Agent('PPOLag', env_id, custom_cfgs=custom_cfgs)
 agent.learn()
 ```
 
-### 2. Run Agent from custom config dict
-
-```python
-import omnisafe
-
-env = 'SafetyPointGoal1-v0'
+### 3. Run Agent from custom terminal config
 
-custom_dict = {'epochs': 1, 'log_dir': './runs'}
-agent = omnisafe.Agent('PPOLag', env, custom_cfgs=custom_dict)
-agent.learn()
-```
+You can also run agent from custom terminal config. You can set any config in corresponding yaml file.
 
-### 3. Run Agent from custom terminal config
+For example, you can run `PPOLag` agent on `SafetyPointGoal1-v0` environment with `total_steps=1024000`, `vector_env_nums=1` and `parallel=1` by:
 
 ```bash
 cd examples
-python train_policy.py --env-id SafetyPointGoal1-v0 --algo PPOLag --parallel 1
+python train_policy.py --algo PPOLag --env-id SafetyPointGoal1-v0 --parallel 1 --total-steps 1024000 --device cpu --vector-env-nums 1 --torch-threads 1
 ```
 
 ### 4. Evalutate Saved Policy
diff --git a/examples/train_from_custom_dict.py b/examples/train_from_custom_dict.py
index d1d6f770c..816c35ffb 100644
--- a/examples/train_from_custom_dict.py
+++ b/examples/train_from_custom_dict.py
@@ -14,44 +14,24 @@
 # ==============================================================================
 """Example of training a policy from custom dict with OmniSafe."""
 
-import argparse
-
 import omnisafe
 
 
-parser = argparse.ArgumentParser()
-env_id = 'SafetyHumanoidVelocity-v4'
-parser.add_argument(
-    '--parallel',
-    default=1,
-    type=int,
-    metavar='N',
-    help='Number of paralleled progress for calculations.',
-)
+env_id = 'SafetyPointGoal1-v0'
 custom_cfgs = {
     'train_cfgs': {
-        'total_steps': 1000,
+        'total_steps': 1024000,
+        'vector_env_nums': 1,
+        '--parallel': 1,
     },
     'algo_cfgs': {
-        'update_cycle': 1000,
+        'update_cycle': 2048,
         'update_iters': 1,
     },
     'logger_cfgs': {
         'use_wandb': False,
     },
-    'env_cfgs': {
-        'vector_env_nums': 1,
-    },
 }
-args, _ = parser.parse_known_args()
-agent = omnisafe.Agent('PPOLag', env_id, custom_cfgs=custom_cfgs, parallel=args.parallel)
-agent.learn()
 
-# obs = env.reset()
-# for i in range(1000):
-#     action, _states = agent.predict(obs, deterministic=True)
-#     obs, reward, done, info = env.step(action)
-#     env.render()
-#     if done:
-#         obs = env.reset()
-# env.close()
+agent = omnisafe.Agent('PPOLag', env_id, custom_cfgs=custom_cfgs)
+agent.learn()
diff --git a/examples/train_from_yaml.py b/examples/train_from_yaml.py
index 7c856cba9..03641150e 100644
--- a/examples/train_from_yaml.py
+++ b/examples/train_from_yaml.py
@@ -16,16 +16,7 @@
 import omnisafe
 
 
-env = omnisafe.Env('SafetyPointGoal1-v0')
+env_id = 'SafetyPointGoal1-v0'
 
-agent = omnisafe.Agent('PPOLag', env)
+agent = omnisafe.Agent('PPOLag', env_id)
 agent.learn()
-
-# obs = env.reset()
-# for i in range(1000):
-#     action, _states = agent.predict(obs, deterministic=True)
-#     obs, reward, cost, done, info = env.step(action)
-#     env.render()
-#     if done:
-#         obs = env.reset()
-# env.close()