  In recent years, quantum-enhanced machine learning has emerged as a
particularly fruitful application of quantum algorithms, covering aspects of
supervised, unsupervised and reinforcement learning. Reinforcement learning
offers numerous options of how quantum theory can be applied, and is arguably
the least explored, from a quantum perspective. Here, an agent explores an
environment and tries to find a behavior optimizing some figure of merit. Some
of the first approaches investigated settings where this exploration can be
sped-up, by considering quantum analogs of classical environments, which can
then be queried in superposition. If the environments have a strict periodic
structure in time (i.e. are strictly episodic), such environments can be
effectively converted to conventional oracles encountered in quantum
information. However, in general environments, we obtain scenarios that
generalize standard oracle tasks. In this work we consider one such
generalization, where the environment is not strictly episodic, which is mapped
to an oracle identification setting with a changing oracle. We analyze this
case and show that standard amplitude-amplification techniques can, with minor
modifications, still be applied to achieve quadratic speed-ups, and that this
approach is optimal for certain settings. This results constitutes one of the
first generalizations of quantum-accessible reinforcement learning.
