  In this lecture, we present a general perspective on reinforcement learning
(RL) objectives, where we show three versions of objectives. The first version
is the standard definition of objective in RL literature. Then we extend the
standard definition to the $\lambda$-return version, which unifies the standard
definition of objective. Finally, we propose a general objective that unifies
the previous two versions. The last version provides a high level to understand
of RL's objective, where it shows a fundamental formulation that connects some
widely used RL techniques (e.g., TD$(\lambda)$ and GAE), and this objective can
be potentially applied to extensive RL algorithms.
