  Existing offline reinforcement learning (RL) algorithms typically assume that
training data is either: 1) generated by a known policy, or 2) of entirely
unknown origin. We consider multi-demonstrator offline RL, a middle ground
where we know which demonstrators generated each dataset, but make no
assumptions about the underlying policies of the demonstrators. This is the
most natural setting when collecting data from multiple human operators, yet
remains unexplored. Since different demonstrators induce different data
distributions, we show that this can be naturally framed as a domain
generalization problem, with each demonstrator corresponding to a different
domain. Specifically, we propose Domain-Invariant Model-based Offline RL
(DIMORL), where we apply Risk Extrapolation (REx) (Krueger et al., 2020) to the
process of learning dynamics and rewards models. Our results show that models
trained with REx exhibit improved domain generalization performance when
compared with the natural baseline of pooling all demonstrators' data. We
observe that the resulting models frequently enable the learning of superior
policies in the offline model-based RL setting, can improve the stability of
the policy learning process, and potentially enable increased exploration.
