  Vision-based deep reinforcement learning (RL) typically obtains performance
benefit by using high capacity and relatively large convolutional neural
networks (CNN). However, a large network leads to higher inference costs
(power, latency, silicon area, MAC count). Many inference optimizations have
been developed for CNNs. Some optimization techniques offer theoretical
efficiency, such as sparsity, but designing actual hardware to support them is
difficult. On the other hand, distillation is a simple general-purpose
optimization technique which is broadly applicable for transferring knowledge
from a trained, high capacity teacher network to an untrained, low capacity
student network. DQN distillation extended the original distillation idea to
transfer information stored in a high performance, high capacity teacher
Q-function trained via the Deep Q-Learning (DQN) algorithm. Our work adapts the
DQN distillation work to the actor-critic Proximal Policy Optimization
algorithm. PPO is simple to implement and has much higher performance than the
seminal DQN algorithm. We show that a distilled PPO student can attain far
higher performance compared to a DQN teacher. We also show that a low capacity
distilled student is generally able to outperform a low capacity agent that
directly trains in the environment. Finally, we show that distillation,
followed by "fine-tuning" in the environment, enables the distilled PPO student
to achieve parity with teacher performance. In general, the lessons learned in
this work should transfer to other modern actor-critic RL algorithms.
