AI BriefingLG AI Research 294
Key point
We propose a new algorithm that improves the stability of offline Learning from Observation (LfO), which imitates expert behavior using only state information.
Details
Existing Imitation Learning (IL) has required expert action information, but humans can learn simply by observing state changes—such as watching driving or videos—without action information. The technique that imitates this is called Learning from Observation (LfO).
Existing offline LfO methods use pre-collected data without interaction with the environment, but two major limitations exist. First, Off-policy LfO algorithms suffer from unstable training due to the use of out-of-distribution (OOD) action values. Second, methods using an Inverse Dynamics Model have limitations in accurately predicting expert actions in stochastic environments.
To address these issues, this study proposes a new formula-based algorithm. By leveraging KL Divergence minimization and regularization techniques, it is designed to achieve optimal performance in offline settings while staying within the data distribution.
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