Abstract: Intelligent agents, interacting with their environment, operate
under constraints on what they can observe and how they can act.
Unbounded agents can use standard Reinforcement Learning to optimize
their inference and control under purely external constraints. Bounded
agents, on the other hand, are subject to internal constraints as well.
This only allows them to partially notice their observations, and to
partially intend their actions, requiring rational selection of
attention and action.In this talk we will see
how to find the optimal information-constrained policy in reactive
(memoryless) agents. We will discuss a number of reasons why internal
constraints are often best modeled as bounds on information-theoretic
quantities, and why we can focus on reactive agents with hardly any loss
of generality. We will link the solution of the constrained problem to
that of soft clustering, and present some of its nice properties, such
as principled dimensionality reduction.