diff --git a/baselines/deepq/build_graph.py b/baselines/deepq/build_graph.py
index f0fcf49..cbafa36 100644
--- a/baselines/deepq/build_graph.py
+++ b/baselines/deepq/build_graph.py
@@ -27,7 +27,7 @@ The functions in this file can are used to create the following functions:
     Function that takes a transition (s,a,r,s') and optimizes Bellman equation's error:
 
         td_error = Q(s,a) - (r + gamma * max_a' Q(s', a'))
-        loss = hauber_loss[td_error]
+        loss = huber_loss[td_error]
 
     Parameters
     ----------
@@ -127,12 +127,12 @@ def build_act(make_obs_ph, q_func, num_actions, scope="deepq", reuse=None):
 
 
 def build_train(make_obs_ph, q_func, num_actions, optimizer, grad_norm_clipping=None, gamma=1.0, double_q=True, scope="deepq", reuse=None):
-    """Creates the act function:
+    """Creates the train function:
 
     Parameters
     ----------
     make_obs_ph: str -> tf.placeholder or TfInput
-        a function that take a name and creates a placeholder of input with that name
+        a function that takes a name and creates a placeholder of input with that name
     q_func: (tf.Variable, int, str, bool) -> tf.Variable
         the model that takes the following inputs:
             observation_in: object
@@ -150,7 +150,7 @@ def build_train(make_obs_ph, q_func, num_actions, optimizer, grad_norm_clipping=
     optimizer: tf.train.Optimizer
         optimizer to use for the Q-learning objective.
     grad_norm_clipping: float or None
-        clip graident norms to this value. If None no clipping is performed.
+        clip gradient norms to this value. If None no clipping is performed.
     gamma: float
         discount rate.
     double_q: bool
diff --git a/baselines/deepq/simple.py b/baselines/deepq/simple.py
index 2e90d71..eafe234 100644
--- a/baselines/deepq/simple.py
+++ b/baselines/deepq/simple.py
@@ -123,11 +123,11 @@ def learn(env,
         final value of random action probability
     train_freq: int
         update the model every `train_freq` steps.
-        set to None to disable printing
     batch_size: int
         size of a batched sampled from replay buffer for training
     print_freq: int
         how often to print out training progress
+        set to None to disable printing
     checkpoint_freq: int
         how often to save the model. This is so that the best version is restored
         at the end of the training. If you do not wish to restore the best version at