diff --git a/docs/index.rst b/docs/index.rst
index 13e57da1143b033a2b216af5e07a4d82672c3198..e7d6c144112fca11f836b6890c68b2e4c2010832 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -60,6 +60,7 @@ Abstractions
 
     getting_started.rst
     new_alg.rst
+    save_param.rst
 
 .. toctree::
     :maxdepth: 2
diff --git a/docs/save_param.rst b/docs/save_param.rst
new file mode 100644
index 0000000000000000000000000000000000000000..3824eb9d3fe23c47f375877a75c6c88aab06c0b4
--- /dev/null
+++ b/docs/save_param.rst
@@ -0,0 +1,26 @@
+Save and Restore Parameters
+=============================
+
+Goal of this tutorial:
+
+- Learn how to save and restore parameters.
+
+Example
+---------------
+
+Sometimes we need to save the parameters into a file and reuse them later on. PARL provides operators 
+to save parameters to a file and restore parameters from a file easily. You only need several lines to implement this.
+
+Here is a demonstration of usage:
+
+.. code-block:: python
+
+    agent = AtariAgent()
+    # save the parameters of agent to ./model.ckpt
+    agent.save('./model.ckpt')             
+    # restore the parameters from ./model.ckpt to agent  
+    agent.restore('./model.ckpt')    
+
+    # restore the parameters from ./model.ckpt to another_agent
+    another_agent =  AtariAgent()
+    another_agent.restore('./model.ckpt')    
diff --git a/examples/QuickStart/train.py b/examples/QuickStart/train.py
index b944e05270fc1a63d7ccaba549436f03d9c35a2f..27de75a08f4c0030b1101b1c40e25046cb484739 100644
--- a/examples/QuickStart/train.py
+++ b/examples/QuickStart/train.py
@@ -15,6 +15,7 @@
 import gym
 import numpy as np
 import parl
+import os.path
 from cartpole_agent import CartpoleAgent
 from cartpole_model import CartpoleModel
 from parl.utils import logger
@@ -51,6 +52,10 @@ def main():
     alg = parl.algorithms.PolicyGradient(model, lr=LEARNING_RATE)
     agent = CartpoleAgent(alg, obs_dim=OBS_DIM, act_dim=ACT_DIM)
 
+    # if the file already exists, restore parameters from it
+    if os.path.exists('./model.ckpt'):
+        agent.restore('./model.ckpt')
+
     for i in range(1000):
         obs_list, action_list, reward_list = run_episode(env, agent)
         if i % 10 == 0:
@@ -67,6 +72,9 @@ def main():
             total_reward = np.sum(reward_list)
             logger.info('Test reward: {}'.format(total_reward))
 
+    # save the parameters to ./model.ckpt
+    agent.save('./model.ckpt')
+
 
 if __name__ == '__main__':
     main()