Imitation Learning with THOR
نویسنده
چکیده
The recently proposed House Of inteRactions (AI2THOR) framework [35] provides an simulation environment for high quality 3D scenes. Together with THOR, a Targetdriven model is introduced to improve generalization capabilities. Imitation learning or learning by demonstration is known to be more effective in communicating task. In this project, we extend the Target-driven model by exploring both established and state-of-the-art imitation learning methods. First we detail our network architecture and training procedure. Then we show that end-to-end deep neural network based imitation learning methods is applicable to the high-dimension environment with raw visual inputs, such as THOR. Finally we analyze our experiments and results.
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