Reinforcement learning provides an effective tool for robots to acquire diverse skills in an automated fashion.For safety and data generation purposes, control policies are often trained in a simulator and later deployed to the target environment, such as a real robot. However, transferring policies across domains is often a manual and tedious process. In order to bridge the gap between domains, it is often necessary to carefully tune and identify the simulator parameters or select the aspects of the simulation environment to randomize. In this paper, we design a novel, adversarial learning algorithm to tackle the transfer problem. We combine a classic, analytical simulator with a differentiable, state-action dependent system identification module that outputs the desired simulator parameters. We then train this hybrid simulator such that the output trajectory distributions are indistinguishable from a target domain collection. The optimized hybrid simulator can refine a sub-optimal policy without any additional target domain data. We show that our approach outperforms the domain-randomization and target-domain refinement baselines on two robots and six difficult dynamic tasks.