  All sequential decision-making agents explore so as to acquire knowledge
about a particular target. It is often the responsibility of the agent designer
to construct this target which, in rich and complex environments, constitutes a
onerous burden; without full knowledge of the environment itself, a designer
may forge a sub-optimal learning target that poorly balances the amount of
information an agent must acquire to identify the target against the target's
associated performance shortfall. While recent work has developed a connection
between learning targets and rate-distortion theory to address this challenge
and empower agents that decide what to learn in an automated fashion, the
proposed algorithm does not optimally tackle the equally important challenge of
efficient information acquisition. In this work, building upon the seminal
design principle of information-directed sampling (Russo & Van Roy, 2014), we
address this shortcoming directly to couple optimal information acquisition
with the optimal design of learning targets. Along the way, we offer new
insights into learning targets from the literature on rate-distortion theory
before turning to empirical results that confirm the value of information when
deciding what to learn.
