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Imitation Learning
Imitation Learning, where decision-making behavior is programmed by demonstration, has led to state-of-the-art performance in a variety of applications, including, e.g., outdoor mobile robot navigation (Silver 2008), legged locomotion (Ratliff 2006), advanced manipulation (Schaal 1999), and electronic games. A common approach to imitation learning is to train a classifier or regressor to replicate an expert's policy given training data of the encountered observations and actions performed by the expert.
Given access to a planner, current state-of-the-art techniques based on Inverse Optimal Control (IOC) (Abbeel 2004 [1], Ratliff 2006) achieve this indirectly by learning the cost function the expert is optimizing from the observed behavior, and the planner is used by the learner to minimize the long-term costs.
The past two decades have seen a paradigm shift towards generative models, which are now becoming state-of-the-art for Imitation learning, like Diffusion Policy.
These techniques can also be thought of as training a classifier (the planner), which is parametrized by the cost function. This often has the advantage that learning the cost function generalizes better over the state space or across similar tasks. A broad spectrum of learning techniques have been applied to imitation learning (Argall 2009; Chernova 2009); however, these applications all violate the crucial assumption made by statistical learning approaches that a learner's prediction does not influence the distribution of examples upon which it will be tested.
References
- Abbeel, Pieter, and Andrew Y. Ng. "Apprenticeship learning via inverse reinforcement learning." Proceedings of the twenty-first international conference on Machine learning. 2004.
- Ross, Stéphane, and Drew Bagnell. "Efficient reductions for imitation learning." Proceedings of the thirteenth international conference on artificial intelligence and statistics. JMLR Workshop and Conference Proceedings, 2010.