diff --git a/rl_orders b/rl_orders
new file mode 100644
index 000000000..7902b901c
Binary files /dev/null and b/rl_orders differ
diff --git a/rl_playground.py b/rl_playground.py
index 482615215..fa2022dcb 100644
--- a/rl_playground.py
+++ b/rl_playground.py
@@ -16,7 +16,7 @@ from qlib.tests.data import GetData
 from qlib.utils import init_instance_by_config, exists_qlib_data
 from torch.utils.data import Dataset, DataLoader
 from tianshou.data import Batch, Collector
-from tianshou.env import DummyVectorEnv
+from tianshou.env import DummyVectorEnv, SubprocVectorEnv
 from tianshou.policy import BasePolicy
 
 
@@ -51,7 +51,8 @@ def price_advantage(exec_price: float, baseline_price: float, direction: int) ->
 @dataclass
 class EpisodicState:
     """
-    A simplified data structure for RL-related components to process observations and rewards
+    A simplified data structure as the input of RL-related components to calculate observations and rewards.
+    Some of the metrics info are calculated on-the-fly in this class.
     """
     # requirements
     stock_id: int
@@ -181,6 +182,7 @@ class SingleOrderEnv(gym.Env):
         return self.observation.observation_space
 
     def retrieve_backtest_data(self, field: str):
+        # Retrieve backtest data for RL-specific use (including reward calculation)
         return D.features(
             [self.cur_order.stock_id],
             ['$open', '$close', '$high', '$low', '$volume'],
@@ -190,6 +192,7 @@ class SingleOrderEnv(gym.Env):
         )[field].to_numpy()
 
     def initialize_state(self):
+        # Synchronous state for executor to EpisodicState
         self.executor.reset(start_time=self.cur_order.start_time, end_time=self.cur_order.end_time)
         state = EpisodicState(
             stock_id=self.cur_order.stock_id,
@@ -207,6 +210,7 @@ class SingleOrderEnv(gym.Env):
         return state
 
     def update_state(self, exec_vol):
+        # Synchronous exec_vol to executor and synchronous back to EpisodicState
         calendar = self.executor.trade_calendar
         state = self.ep_state
 
@@ -273,6 +277,7 @@ class SingleOrderEnv(gym.Env):
                 'ins': self.ep_state.stock_id,
                 'date': self.ep_state.start_time,
             }
+            # TODO: collect logs
             pprint(info)
 
         return self.observation(self.ep_state), reward, self.ep_state.done, info
@@ -327,13 +332,18 @@ def _main():
         )
         return SingleOrderEnv(
             observation, action, reward_fn,
-            iter(DataLoader(QlibOrderDataset('rl.pkl'), batch_size=None, shuffle=True)), executor)
+            iter(DataLoader(QlibOrderDataset('rl_orders'), batch_size=None, shuffle=True)), executor)
 
     policy = DummyPolicy()
 
+    # This can not be replaced with SubprocVectorEnv
+    # File "/xxx/qlib/qlib/data/data.py", line 462, in dataset_processor
+    # p = Pool(processes=workers)
+    # AssertionError: daemonic processes are not allowed to have children
     envs = DummyVectorEnv([dummy_env for _ in range(4)])
     test_collector = Collector(policy, envs)
     policy.eval()
+    # TODO: create a queue for all orders and make it auto-complete when all the orders are processed
     test_collector.collect(n_episode=10)