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.

本文介绍了一种多演示者离线强化学习算法，该算法能够自然地解决不同演示者产生不同数据分布的问题，并提出了一种基于 Risk Extrapolation 的领域不变模型离线强化学习方法，结果表明，该方法能够提高领域泛化性能，可以改善策略学习过程的稳定性，并可以潜在地实现探索增强。

针对鲁棒的基于模型的离线强化学习的领域通用性

Domain Generalization for Robust Model-Based Offline Reinforcement Learning

Distributional shift is one of the major obstacles when transferring machine
learning prediction systems from the lab to the real world. To tackle this
problem, we assume that variation across training domains is representative of
the variation we might encounter at test time, but also that shifts at test
time may be more extreme in magnitude. In particular, we show that reducing
differences in risk across training domains can reduce a model's sensitivity to
a wide range of extreme distributional shifts, including the challenging
setting where the input contains both causal and anti-causal elements. We
motivate this approach, Risk Extrapolation (REx), as a form of robust
optimization over a perturbation set of extrapolated domains (MM-REx), and
propose a penalty on the variance of training risks (V-REx) as a simpler
variant. We prove that variants of REx can recover the causal mechanisms of the
targets, while also providing some robustness to changes in the input
distribution ("covariate shift"). By appropriately trading-off robustness to
causally induced distributional shifts and covariate shift, REx is able to
outperform alternative methods such as Invariant Risk Minimization in
situations where these types of shift co-occur.

采用风险外推法的鲁棒优化可以在训练域和测试域之间进行平衡，从而提高模型对于分布漂移的抵抗能力，并且可以恢复目标的因果机制，同时还能提供对于输入分布变化具有一定鲁棒性的能力。