  We introduce a new recurrent agent architecture and associated auxiliary
losses which improve reinforcement learning in partially observable tasks
requiring long-term memory. We employ a temporal hierarchy, using a
slow-ticking recurrent core to allow information to flow more easily over long
time spans, and three fast-ticking recurrent cores with connections designed to
create an information asymmetry. The \emph{reaction} core incorporates new
observations with input from the slow core to produce the agent's policy; the
\emph{perception} core accesses only short-term observations and informs the
slow core; lastly, the \emph{prediction} core accesses only long-term memory.
An auxiliary loss regularizes policies drawn from all three cores against each
other, enacting the prior that the policy should be expressible from either
recent or long-term memory. We present the resulting
\emph{Perception-Prediction-Reaction} (PPR) agent and demonstrate its improved
performance over a strong LSTM-agent baseline in DMLab-30, particularly in
tasks requiring long-term memory. We further show significant improvements in
Capture the Flag, an environment requiring agents to acquire a complicated
mixture of skills over long time scales. In a series of ablation experiments,
we probe the importance of each component of the PPR agent, establishing that
the entire, novel combination is necessary for this intriguing result.
