LLM-guided probabilistic program induction can learn low-complexity POMDP models from ten demonstrations and outperform tabular learning, behavior cloning, and direct LLM planning in simulated and real robot domains.
Learning nonsingular phylogenies and hidden Markov models
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abstract
In this paper we study the problem of learning phylogenies and hidden Markov models. We call a Markov model nonsingular if all transition matrices have determinants bounded away from 0 (and 1). We highlight the role of the nonsingularity condition for the learning problem. Learning hidden Markov models without the nonsingularity condition is at least as hard as learning parity with noise, a well-known learning problem conjectured to be computationally hard. On the other hand, we give a polynomial-time algorithm for learning nonsingular phylogenies and hidden Markov models.
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LLM-Guided Probabilistic Program Induction for POMDP Model Estimation
LLM-guided probabilistic program induction can learn low-complexity POMDP models from ten demonstrations and outperform tabular learning, behavior cloning, and direct LLM planning in simulated and real robot domains.