Policy Distillation
Abstract: Policies for complex visual tasks have been successfully learned with deep reinforcement learning, using an approach called deep Q networks (DQN) but relatively large (task specific) networks and extensive training are needed to achieve good performance. In this work, we present a novel method called policy distillation that can be used to extract the policy of a reinforcement learning agent and train a new network that performs at the expert level while being dramatically smaller and more efficient. Furthermore, the same method can be used to consolidate multiple task specific policies into a single policy. We demonstrate these claims using the Atari domain and show that the multi task distilled agent outperforms the single task teachers as well as a jointly trained DQN agent.
ICLR