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Multi-agent autonomous racing is a challenging problem for autonomous vehicles due to the split-second, and complex decisions that vehicles must continuously make during a race. The presence of other agents on the track requires continuous monitoring of the ego vehicle’s surroundings, and necessitates predicting the behavior of other vehicles so the ego can quickly react to a changing environment with informed decisions. In our previous work we have developed the DeepRacing AI framework for autonomous formula one racing. Our DeepRacing framework was the first implementation to use the highly photorealisitc Formula One game as a simulation testbed for autonomous racing. We have successfully demonstrated single agent high speed autonomous racing using Bezier curve trajectories. In this paper, we extend the capabilities of the DeepRacing framework towards multi-agent autonomous racing. To do so, we first develop and learn a virtual camera model from game data that the user can configure to emulate the presence of a camera sensor on the vehicle. Next we propose and train a deep recurrent neural network that can predict the future poses of opponent agents in the field of view of the virtual camera using vehicles position, velocity, and heading data with respect to the ego vehicle racecar. We demonstrate early promising results for both these contributions in the game. These added features will extend the DeepRacing framework to become more suitable for multi-agent autonomous racing algorithm development