  Many real-world applications such as robotics provide hard constraints on
power and compute that limit the viable model complexity of Reinforcement
Learning (RL) agents. Similarly, in many distributed RL settings, acting is
done on un-accelerated hardware such as CPUs, which likewise restricts model
size to prevent intractable experiment run times. These "actor-latency"
constrained settings present a major obstruction to the scaling up of model
complexity that has recently been extremely successful in supervised learning.
To be able to utilize large model capacity while still operating within the
limits imposed by the system during acting, we develop an "Actor-Learner
Distillation" (ALD) procedure that leverages a continual form of distillation
that transfers learning progress from a large capacity learner model to a small
capacity actor model. As a case study, we develop this procedure in the context
of partially-observable environments, where transformer models have had large
improvements over LSTMs recently, at the cost of significantly higher
computational complexity. With transformer models as the learner and LSTMs as
the actor, we demonstrate in several challenging memory environments that using
Actor-Learner Distillation recovers the clear sample-efficiency gains of the
transformer learner model while maintaining the fast inference and reduced
total training time of the LSTM actor model.
