[AMR] Many learnings from exercises

electives/amr/parts/05_planning-control/02_learning-to-act/02_reinforcement-learning.tex

@@ -21,7 +21,7 @@ Then update: $Q_{i + 1}(\vec{x}, \vec{u}) = (1 - \alpha)Q_i(\vec{x}, \vec{u}) +
 Called off-policy learning, needs exploration. Simplest is random actions ($\varepsilon$-greedy):
 $\varepsilon$ is prob. to act randomly, $1 - \varepsilon$ is prob. to act on pol. {\color{red} Space explored, still doing random stuff}
 
-\bi{Approaches} 
+\bi{DL-Approaches}
 \textit{Model-based} (estimate trans. model, e.g. Dyna),
 Value-based (estimate val or $Q$-func and extract pol., e.g. Q-Learn),
 Actor-Critic (estim. val or $Q$ of curr. pol., improve pol., e.g. A3C, SAC),