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Methods for generalization in model-based reinforcement learning typically assume that an agent cannot recover the latent context governing the environment dynamics from its own experience, and therefore supplies it externally. We formalize and test this assumption with \emph{predictive sufficiency}, which quantifies w...
This survey systematically review and synthesize SSL methodologies tailored for ES modeling across multiple domains, bridging the gaps between domain-specific approaches that have traditionally operated in isolation and identifies critical gaps in current research.
The reason for low performance is analyzed and a covariance corrected Mahalanobis distance is proposed for detecting out-of-domain intents, outperforming competing approaches.
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