Clarify algorithm discussion

Author: nickswalker

report/report.tex

@@ -83,13 +83,13 @@
 
 	\subsubsection{Function Approximation}
 
-	Though it may initially seem like the microcontroller could support a reasonable number of features, inspection reveals this is not the case. Consider the episodic semi-gradient one-step Sarsa algorithm.
+	The microcontroller cannot support as many features as one might hope. Consider the episodic semi-gradient one-step Sarsa algorithm.
 
 	\begin{equation}\label{eqn:update}
 	\bm{\theta}_{t+1} = \bm{\theta}_t + \alpha \Big[R_{t+1} + \gamma \hat{q}(S_{t+1}, A_{t+1}\, \bm{\theta}_t) - \hat{q}(S_t, A_t, \bm{\theta}_t)\Big]\Delta\hat{q}(S_t, A_t, \bm{\theta}_t)\tag{1}
 	\end{equation}
 
-	As shown in the implementation, it is possible to implement the update using only $n$ additional space, where $n$ is the number of weights. While this implementation is not difficult, it is easy to do incorrectly. For instance, if the action selection step is not placed before the memory allocation, the implementation will actually consume $2n$ stack memory; maximizing the value function over possible next states requires an additional $n$ stack space.
+	As shown in the implementation, it is possible to implement the update using only $n$ additional space, where $n$ is the number of weights, but it is easy to do incorrectly. For instance, if the action selection step is not placed before the memory allocation, the implementation will actually consume $2n$ stack memory; maximizing the value function over possible next states requires an additional $n$ stack space.
 
 		\begin{algorithm}
 			\caption{Memory-conscious Episodic Semi-gradient One-step Sarsa}