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Imitation Learning (IL) is an important paradigm within the broaderreinforcement learning (RL) methodology. Unlike most of RL, it does not assumeavailability of reward-feedback. Reward inference and shaping are known to bedifficult and error-prone methods particularly when the demonstration datacomes from human experts. Classical methods such as behavioral cloning andinverse reinforcement learning are highly sensitive to estimation errors, aproblem that is particularly acute in continuous state space problems.Meanwhile, state-of-the-art IL algorithms convert behavioral policy learningproblems into distribution-matching problems which often require additionalonline interaction data to be effective. In this paper, we consider the problemof imitation learning in continuous state space environments based solely onobserved behavior, without access to transition dynamics information, rewardstructure, or, most importantly, any additional interactions with theenvironment. Our approach is based on the Markov balance equation andintroduces a novel conditional kernel density estimation-based imitationlearning framework. It involves estimating the environment's transitiondynamics using conditional kernel density estimators and seeks to satisfy theprobabilistic balance equations for the environment. We establish that ourestimators satisfy basic asymptotic consistency requirements. Through a seriesof numerical experiments on continuous state benchmark environments, we showconsistently superior empirical performance over many state-of-the-art ILalgorithms.

Conditional Kernel Imitation Learning for Continuous State Environments