  Despite the recent success of deep reinforcement learning (RL), domain
adaptation remains an open problem. Although the generalization ability of RL
agents is critical for the real-world applicability of Deep RL, zero-shot
policy transfer is still a challenging problem since even minor visual changes
could make the trained agent completely fail in the new task. To address this
issue, we propose a two-stage RL agent that first learns a latent unified state
representation (LUSR) which is consistent across multiple domains in the first
stage, and then do RL training in one source domain based on LUSR in the second
stage. The cross-domain consistency of LUSR allows the policy acquired from the
source domain to generalize to other target domains without extra training. We
first demonstrate our approach in variants of CarRacing games with customized
manipulations, and then verify it in CARLA, an autonomous driving simulator
with more complex and realistic visual observations. Our results show that this
approach can achieve state-of-the-art domain adaptation performance in related
RL tasks and outperforms prior approaches based on latent-representation based
RL and image-to-image translation.
