  Reinforcement learning aims to learn optimal policies from interaction with
environments whose dynamics are unknown. Many methods rely on the approximation
of a value function to derive near-optimal policies. In partially observable
environments, these functions depend on the complete sequence of observations
and past actions, called the history. In this work, we show empirically that
recurrent neural networks trained to approximate such value functions
internally filter the posterior probability distribution of the current state
given the history, called the belief. More precisely, we show that, as a
recurrent neural network learns the Q-function, its hidden states become more
and more correlated with the beliefs of state variables that are relevant to
optimal control. This correlation is measured through their mutual information.
In addition, we show that the expected return of an agent increases with the
ability of its recurrent architecture to reach a high mutual information
between its hidden states and the beliefs. Finally, we show that the mutual
information between the hidden states and the beliefs of variables that are
irrelevant for optimal control decreases through the learning process. In
summary, this work shows that in its hidden states, a recurrent neural network
approximating the Q-function of a partially observable environment reproduces a
sufficient statistic from the history that is correlated to the relevant part
of the belief for taking optimal actions.
