Learning non-Markovian Decision-Making from State-only Sequences

• URL: http://arxiv.org/abs/2306.15156v3
• Date: Mon, 30 Oct 2023 06:18:02 GMT
• Title: Learning non-Markovian Decision-Making from State-only Sequences
• Authors: Aoyang Qin, Feng Gao, Qing Li, Song-Chun Zhu, Sirui Xie
• Abstract summary: We develop a model-based imitation of state-only sequences with non-Markov Decision Process (nMDP) We demonstrate the efficacy of the proposed method in a path planning task with non-Markovian constraints.
• Score: 57.20193609153983
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Conventional imitation learning assumes access to the actions of demonstrators, but these motor signals are often non-observable in naturalistic settings. Additionally, sequential decision-making behaviors in these settings can deviate from the assumptions of a standard Markov Decision Process (MDP). To address these challenges, we explore deep generative modeling of state-only sequences with non-Markov Decision Process (nMDP), where the policy is an energy-based prior in the latent space of the state transition generator. We develop maximum likelihood estimation to achieve model-based imitation, which involves short-run MCMC sampling from the prior and importance sampling for the posterior. The learned model enables \textit{decision-making as inference}: model-free policy execution is equivalent to prior sampling, model-based planning is posterior sampling initialized from the policy. We demonstrate the efficacy of the proposed method in a prototypical path planning task with non-Markovian constraints and show that the learned model exhibits strong performances in challenging domains from the MuJoCo suite.

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