Develops infinite-horizon stationary robust mean-field games incorporating distributional uncertainty, proves equilibrium existence via fixed-point on contractive Bellman operator, gives convergent algorithm, and derives finite-population approximation bounds under contractive regime.
arXiv preprint arXiv:1807.06158 , year=
3 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Imitation from observation (IfO) is the problem of learning directly from state-only demonstrations without having access to the demonstrator's actions. The lack of action information both distinguishes IfO from most of the literature in imitation learning, and also sets it apart as a method that may enable agents to learn from a large set of previously inapplicable resources such as internet videos. In this paper, we propose both a general framework for IfO approaches and also a new IfO approach based on generative adversarial networks called generative adversarial imitation from observation (GAIfO). We conduct experiments in two different settings: (1) when demonstrations consist of low-dimensional, manually-defined state features, and (2) when demonstrations consist of high-dimensional, raw visual data. We demonstrate that our approach performs comparably to classical imitation learning approaches (which have access to the demonstrator's actions) and significantly outperforms existing imitation from observation methods in high-dimensional simulation environments.
years
2026 3verdicts
UNVERDICTED 3representative citing papers
TRIRL uses a trust-region insight to allow explicit dual ascent in IRL with local policy searches, claiming monotonic improvement and better generalization than prior methods.
XIPER creates a reward signal for cross-domain video imitation learning by training a video prediction model that maps agent views to the expert domain and scoring prediction likelihood.
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Stationary Robust Mean-Field Games under Model Mismatches
Develops infinite-horizon stationary robust mean-field games incorporating distributional uncertainty, proves equilibrium existence via fixed-point on contractive Bellman operator, gives convergent algorithm, and derives finite-population approximation bounds under contractive regime.
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Trust Region Inverse Reinforcement Learning: Explicit Dual Ascent using Local Policy Updates
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