diff --git a/lab3/RL.ipynb b/lab3/RL.ipynb
index 05abf42d..e1c9dcb0 100644
--- a/lab3/RL.ipynb
+++ b/lab3/RL.ipynb
@@ -365,7 +365,7 @@
       "source": [
         "## 3.3 Reward function\n",
         "\n",
-        "We're almost ready to begin the learning algorithm for our agent! The next step is to compute the rewards of our agent as it acts in the environment. Since we (and the agent) is uncertain about if and when the game or task will end (i.e., when the pole will fall), it is useful to emphasize getting rewards **now** rather than later in the future -- this is the idea of discounting. This is a similar concept to discounting money in the case of interest. ecall from lecture, we use reward discount to give more preference at getting rewards now rather than later in the future. The idea of discounting rewards is similar to discounting money in the case of interest.\n",
+        "We're almost ready to begin the learning algorithm for our agent! The next step is to compute the rewards of our agent as it acts in the environment. Since we (and the agent) is uncertain about if and when the game or task will end (i.e., when the pole will fall), it is useful to emphasize getting rewards **now** rather than later in the future -- this is the idea of discounting. This is a similar concept to discounting money in the case of interest. Recall from lecture, we use reward discount to give more preference at getting rewards now rather than later in the future. The idea of discounting rewards is similar to discounting money in the case of interest.\n",
         "\n",
         "To compute the expected cumulative reward, known as the **return**, at a given timestep in a learning episode, we sum the discounted rewards expected at that time step $t$, within a learning episode, and projecting into the future. We define the return (cumulative reward) at a time step $t$, $R_{t}$ as:\n",
         "\n",
@@ -1109,4 +1109,4 @@
       ]
     }
   ]
-}
\ No newline at end of file
+}