Reinforcement learning has proven capable of extending the applicability of machine learning to domains in which
knowledge cannot be acquired from labeled examples but only via trial-and-error. Being able to solve problems with such
characteristics is a crucial requirement for autonomous agents that can accomplish tasks without human intervention.
However, most reinforcement learning algorithms are designed to solve exactly one task, not offering means to systematically
reuse previous knowledge acquired in other problems. Motivated by insights from homotopic continuation methods,
in this work we investigate approaches based on optimization- and concurrent systems theory to gain an understanding
of conceptual and technical challenges of knowledge transfer in reinforcement learning domains. Building upon these
findings, we present an algorithm based on contextual relative entropy policy search that allows an agent to generate
a structured sequence of learning tasks that guide its learning towards a target distribution of tasks by giving it control
over an otherwise hidden context distribution. The presented algorithm is evaluated on a number of robotic tasks, in
which a desired system state needs to be reached, demonstrating that the proposed learning scheme helps to increase
and stabilize learning performance
