  Reinforcement learning aims at searching the best policy model for decision
making, and has been shown powerful for sequential recommendations. The
training of the policy by reinforcement learning, however, is placed in an
environment. In many real-world applications, however, the policy training in
the real environment can cause an unbearable cost, due to the exploration in
the environment. Environment reconstruction from the past data is thus an
appealing way to release the power of reinforcement learning in these
applications. The reconstruction of the environment is, basically, to extract
the casual effect model from the data. However, real-world applications are
often too complex to offer fully observable environment information. Therefore,
quite possibly there are unobserved confounding variables lying behind the
data. The hidden confounder can obstruct an effective reconstruction of the
environment. In this paper, by treating the hidden confounder as a hidden
policy, we propose a deconfounded multi-agent environment reconstruction
(DEMER) approach in order to learn the environment together with the hidden
confounder. DEMER adopts a multi-agent generative adversarial imitation
learning framework. It proposes to introduce the confounder embedded policy,
and use the compatible discriminator for training the policies. We then apply
DEMER in an application of driver program recommendation. We firstly use an
artificial driver program recommendation environment, abstracted from the real
application, to verify and analyze the effectiveness of DEMER. We then test
DEMER in the real application of Didi Chuxing. Experiment results show that
DEMER can effectively reconstruct the hidden confounder, and thus can build the
environment better. DEMER also derives a recommendation policy with a
significantly improved performance in the test phase of the real application.
