  While reinforcement learning algorithms provide automated acquisition of
optimal policies, practical application of such methods requires a number of
design decisions, such as manually designing reward functions that not only
define the task, but also provide sufficient shaping to accomplish it. In this
paper, we view reinforcement learning as inferring policies that achieve
desired outcomes, rather than as a problem of maximizing rewards. To solve this
inference problem, we establish a novel variational inference formulation that
allows us to derive a well-shaped reward function which can be learned directly
from environment interactions. From the corresponding variational objective, we
also derive a new probabilistic Bellman backup operator and use it to develop
an off-policy algorithm to solve goal-directed tasks. We empirically
demonstrate that this method eliminates the need to hand-craft reward functions
for a suite of diverse manipulation and locomotion tasks and leads to effective
goal-directed behaviors.

Machine Learning;Artificial Intelligence;Methodology;Machine Learning