  The capability to widely sample the state and action spaces is a key
ingredient toward building effective reinforcement learning algorithms. The
variational optimization principles exposed in this paper emphasize the
importance of an occupancy model to synthesizes the general distribution of the
agent's environmental states over which it can act (defining a virtual
``territory''). The occupancy model is the subject of frequent updates as the
exploration progresses and that new states are undisclosed during the course of
the training. By making a uniform prior assumption, the resulting objective
expresses a balance between two concurrent tendencies, namely the widening of
the occupancy space and the maximization of the rewards, reminding of the
classical exploration/exploitation trade-off. Implemented on an actor-critic
off-policy on classic continuous action benchmarks, it is shown to provide
significant increase in the sampling efficacy, that is reflected in a reduced
training time and higher returns, in both the dense and the sparse rewards
cases.
