  Present-day Deep Reinforcement Learning (RL) systems show great promise
towards building intelligent agents surpassing human-level performance.
However, the computational complexity associated with the underlying deep
neural networks (DNNs) leads to power-hungry implementations. This makes deep
RL systems unsuitable for deployment on resource-constrained edge devices. To
address this challenge, we propose a reconfigurable architecture with
preemptive exits for efficient deep RL (RAPID-RL). RAPID-RL enables conditional
activation of DNN layers based on the difficulty level of inputs. This allows
to dynamically adjust the compute effort during inference while maintaining
competitive performance. We achieve this by augmenting a deep Q-network (DQN)
with side-branches capable of generating intermediate predictions along with an
associated confidence score. We also propose a novel training methodology for
learning the actions and branch confidence scores in a dynamic RL setting. Our
experiments evaluate the proposed framework for Atari 2600 gaming tasks and a
realistic Drone navigation task on an open-source drone simulator (PEDRA). We
show that RAPID-RL incurs 0.34x (0.25x) number of operations (OPS) while
maintaining performance above 0.88x (0.91x) on Atari (Drone navigation) tasks,
compared to a baseline-DQN without any side-branches. The reduction in OPS
leads to fast and efficient inference, proving to be highly beneficial for the
resource-constrained edge where making quick decisions with minimal compute is
essential.
