change model.ckpt to model_dir

docs/tutorial/save_param.rst

@@ -16,11 +16,11 @@ 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')    
+    # save the parameters of agent to ./model_dir
+    agent.save('./model_dir')             
+    # restore the parameters from ./model_dir to agent  
+    agent.restore('./model_dir')    
 
-    # restore the parameters from ./model.ckpt to another_agent
+    # restore the parameters from ./model_dir to another_agent
     another_agent = AtariAgent()
-    another_agent.restore('./model.ckpt')    
+    another_agent.restore('./model_dir')    

docs/zh_CN/tutorial/param.md

@@ -4,10 +4,10 @@
 当用户构建好agent之后，可以直接通过agent的相关接口来完成参数的存储。
 ```python
 agent = AtariAgent()
-# 保存参数到 ./model.ckpt
-agent.save('./model.ckpt')
+# 保存参数到 ./model_dir
+agent.save('./model_dir')
 # 恢复参数到这个agent上
-agent.restore('./model.ckpt')
+agent.restore('./model_dir')
 ```
 
 场景2: 并行训练过程中，经常需要把最新的模型参数同步到另一台服务器上，这时候，需要把模型参数拿到内存中，然后再赋值给另一台机器上的agent（actor)。

examples/MADDPG/train.py

@@ -121,10 +121,10 @@ def train_agent():
     if args.restore:
         # restore modle
         for i in range(len(agents)):
-            model_file = args.model_dir + '/agent_' + str(i) + '.ckpt'
+            model_file = args.model_dir + '/agent_' + str(i)
             if not os.path.exists(model_file):
-                logger.info('model file {} does not exits'.format(model_file))
-                raise Exception
+                raise Exception(
+                    'model file {} does not exits'.format(model_file))
             agents[i].restore(model_file)
 
     t_start = time.time()
@@ -166,7 +166,7 @@ def train_agent():
             if not args.restore:
                 os.makedirs(os.path.dirname(args.model_dir), exist_ok=True)
                 for i in range(len(agents)):
-                    model_name = '/agent_' + str(i) + '.ckpt'
+                    model_name = '/agent_' + str(i)
                     agents[i].save(args.model_dir + model_name)
 
 

examples/QuickStart/train.py

@@ -57,8 +57,8 @@ def main():
     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')
+    if os.path.exists('./model_dir'):
+        agent.restore('./model_dir')
 
     for i in range(1000):
         obs_list, action_list, reward_list = run_episode(env, agent)
@@ -76,8 +76,8 @@ 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')
+    # save the parameters to ./model_dir
+    agent.save('./model_dir')
 
 
 if __name__ == '__main__':

examples/offline-Q-learning/parallel_run.py

@@ -97,15 +97,15 @@ def main():
         model, act_dim=act_dim, gamma=GAMMA, lr=LEARNING_RATE * gpu_num)
     agent = AtariAgent(
         algorithm, act_dim=act_dim, total_step=args.train_total_steps)
-    if os.path.isfile('./model.ckpt'):
+    if os.path.isfile('./model_dir'):
         logger.info("load model from file")
-        agent.restore('./model.ckpt')
+        agent.restore('./model_dir')
 
     if args.train:
         logger.info("train with memory data")
         run_train_step(agent, rpm)
         logger.info("finish training. Save the model.")
-        agent.save('./model.ckpt')
+        agent.save('./model_dir')
     else:
         logger.info("collect experience")
         collect_exp(env, rpm, agent)

parl/core/fluid/agent.py

@@ -21,6 +21,7 @@ from parl.core.fluid import layers
 from parl.core.agent_base import AgentBase
 from parl.core.fluid.algorithm import Algorithm
 from parl.utils import machine_info
+from parl.utils import logger
 
 __all__ = ['Agent']
 
@@ -147,13 +148,14 @@ class Agent(AgentBase):
         .. code-block:: python
 
             agent = AtariAgent()
-            agent.save()
-            agent.save('./program_model')
-            agent.save('./program_model', program=agent.learn_program)
+            agent.save('./model_dir')
+            agent.save('./model_dir', program=agent.learn_program)
 
         """
-        if save_path is None:
-            save_path = './program_model'
+        assert save_path is not None, 'please specify `save_path` '
+        if os.path.isfile(save_path):
+            raise Exception('can not save to {}, it is a file, not directory'.
+                            format(save_path))
         if not os.path.exists(save_path):
             os.makedirs(save_path)
         all_programs = [
@@ -189,7 +191,6 @@ class Agent(AgentBase):
 
     def restore(self, save_path=None, program=None):
         """Restore previously saved parameters from save_path. 
-        default save_path is  ./program_model
 
         Args:
             save_path(str): path where parameters were previously saved.
@@ -203,12 +204,11 @@ class Agent(AgentBase):
         .. code-block:: python
 
             agent = AtariAgent()
-            agent.save()
-            agent.restore()
+            agent.save('./model_dir')
+            agent.restore('./model_dir')
 
         """
-        if save_path is None:
-            save_path = './program_model'
+        assert save_path is not None, 'please specify `save_path` '
         if not os.path.exists(save_path):
             raise Exception(
                 'can not restore from {}, directory does not exists'.format(

parl/core/fluid/tests/agent_base_test.py

@@ -92,8 +92,8 @@ class AgentBaseTest(unittest.TestCase):
         agent = TestAgent(self.alg)
         obs = np.random.random([3, 10]).astype('float32')
         output_np = agent.predict(obs)
-        save_path1 = 'model.ckpt'
-        save_path2 = os.path.join('my_model', 'model-2.ckpt')
+        save_path1 = 'model_dir'
+        save_path2 = os.path.join('my_model', 'model-2_dir')
         agent.save(save_path1)
         agent.save(save_path2)
         self.assertTrue(os.path.exists(save_path1))
@@ -103,7 +103,7 @@ class AgentBaseTest(unittest.TestCase):
         agent = TestAgent(self.alg)
         obs = np.random.random([3, 10]).astype('float32')
         output_np = agent.predict(obs)
-        save_path1 = 'model.ckpt'
+        save_path1 = 'model_dir'
         previous_output = agent.predict(obs)
         agent.save(save_path1)
         agent.restore(save_path1)
@@ -121,7 +121,7 @@ class AgentBaseTest(unittest.TestCase):
         agent.learn_program = parl.compile(agent.learn_program)
         obs = np.random.random([3, 10]).astype('float32')
         previous_output = agent.predict(obs)
-        save_path1 = 'model.ckpt'
+        save_path1 = 'model_dir'
         agent.save(save_path1)
         agent.restore(save_path1)