Title: Adversarial Counterfactual Environment Model Learning. Abstract: A good model for action-effect prediction, i.e., the environment model, is essential for sample-efficient policy learning, in which the agent can take numerous free trials to find good policies. Currently, the model is commonly learned by fitting historical transition data through empirical risk minimization (ERM). However, we discover that simple data fitting can lead to a model that will be totally wrong in guiding policy learning due to the selection bias in offline dataset collection. In this work, we introduce weighted empirical risk minimization (WERM) to handle this problem in model learning.  A typical WERM method utilizes inverse propensity scores to re-weight the training data to approximate the target distribution. However, during the policy training, the data distributions of the candidate policies can be various and unknown. Thus, we propose an adversarial weighted empirical risk minimization (AWRM) objective that learns the model with respect to the worst case of the target distributions. We implement AWRM in a sequential decision structure, resulting in the GALILEO model learning algorithm. We also discover that GALILEO is closely related to adversarial model learning, explaining the empirical effectiveness of the latter. We apply GALILEO in synthetic tasks and verify that GALILEO makes accurate predictions on counterfactual data. We finally applied GALILEO in real-world offline policy learning tasks and found that GALILEO significantly improves policy performance in real-world testing.