We propose a new transfer learning algorithm between tasks with different dynamics. The proposed algorithm solves an Imitation from Observation problem (IfO) to ground the source environment to the target task before learning an optimal policy in the grounded environment. The learned policy is deployed in the target task without additional training. A particular feature of our algorithm is the employment of multiple rollout policies during training with a goal to ground the environment more globally; hence, it is named as Multi-Policy Grounding (MPG). The quality of final policy is further enhanced via ensemble policy learning. We demonstrate the superiority of the proposed algorithm analytically and numerically. Numerical studies show that the proposed multi-policy approach allows comparable grounding with single policy approach with a fraction of target samples, hence the algorithm is able to maintain the quality of obtained policy even as the number of interactions with the target environment becomes extremely small.

Many disasters have occurred around the world and have caused sizable damage. A disaster, called a mass casualty incident (MCI), generates a large number of casualties that overwhelm the capacity of local medical resources, and the disaster responses to the MCI requires many interactions among the disaster responders. To evaluate the efficiency of the disaster responses against MCIs, this paper proposes an agent-based model describing the cooperations among the responders during the overall process in the disaster responses from transporting patients to their definitive care. In particular, the proposed model includes geospatial details, such as the road network and the location of hospitals around the disaster scene, and medical information, such as the distribution of medical resources and transporting units, in the region of interest to discover the key factors of the disaster response system that customized to the target region. The case study in this paper presents that the proposed approach was applied to describe a disaster response system and illustrates how the additional details are utilized to analyze the disaster response system. We expect that the proposed method can provide comprehensive insights to a disaster response system of interest, and it can be used as groundwork for improving the disaster response system.