Novel Perspectives in Imitation Learning: Trends, Challenges, Future Directions

CH.E.N. Sai Priya; Manas Kumar Yogi

Abstract

Imitation learning refers to a machine learning approach where an algorithm learns to accomplish a task by emulating the actions and behaviours of a proficient expert. The main aim of imitation learning is to extract knowledge from an expert and train the datasets using the existing knowledge. During training, the algorithm is presented with a large dataset of expert demonstrations and learns to map the input to the correct output based on the examples it has seen. The learned mapping can then be used to perform the task on new inputs that were not present in the training dataset. One example of imitation learning is training an autonomous car to drive by watching and imitating a human driver. In the past few years, various trends in imitation learning have emerged, significantly influencing the trajectory of this field, and shaping its future. These trends include development, integration, and transfer learning. These trends in imitation learning are helping to make this technique more efficient, effective, and applicable to a broader range of tasks and environments. Over the past few years, the imitation learning field has experienced the emergence of multiple trends that have had a substantial impact on its direction and are actively shaping its future. The development of new and more efficient and scalable methods for imitation learning could enable it to be applied to even larger and more complex problems and increase the scope of the problems. In this study, we provide a literature review of trends, challenges, and future developments in imitation learning.

Abstract

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