  The success of deep reinforcement learning (DRL) hinges on the availability
of training data, which is typically obtained via a large number of environment
interactions. In many real-world scenarios, costs and risks are associated with
gathering these data. The field of offline reinforcement learning addresses
these issues through outsourcing the collection of data to a domain expert or a
carefully monitored program and subsequently searching for a batch-constrained
optimal policy. With the emergence of data markets, an alternative to
constructing a dataset in-house is to purchase external data. However, while
state-of-the-art offline reinforcement learning approaches have shown a lot of
promise, they currently rely on carefully constructed datasets that are well
aligned with the intended target domains. This raises questions regarding the
transferability and robustness of an offline reinforcement learning agent
trained on externally acquired data. In this paper, we empirically evaluate the
ability of the current state-of-the-art offline reinforcement learning
approaches to coping with the source-target domain mismatch within two MuJoCo
environments, finding that current state-of-the-art offline reinforcement
learning algorithms underperform in the target domain. To address this, we
propose data valuation for offline reinforcement learning (DVORL), which allows
us to identify relevant and high-quality transitions, improving the performance
and transferability of policies learned by offline reinforcement learning
algorithms. The results show that our method outperforms offline reinforcement
learning baselines on two MuJoCo environments.
