  In this paper, we consider the important problem of safe exploration in
reinforcement learning. While reinforcement learning is well-suited to domains
with complex transition dynamics and high-dimensional state-action spaces, an
additional challenge is posed by the need for safe and efficient exploration.
Traditional exploration techniques are not particularly useful for solving
dangerous tasks, where the trial and error process may lead to the selection of
actions whose execution in some states may result in damage to the learning
system (or any other system). Consequently, when an agent begins an interaction
with a dangerous and high-dimensional state-action space, an important question
arises; namely, that of how to avoid (or at least minimize) damage caused by
the exploration of the state-action space. We introduce the PI-SRL algorithm
which safely improves suboptimal albeit robust behaviors for continuous state
and action control tasks and which efficiently learns from the experience
gained from the environment. We evaluate the proposed method in four complex
tasks: automatic car parking, pole-balancing, helicopter hovering, and business
management.
