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.

翻译：传统模仿学习假设能够获取演示者的动作信息，但在自然场景中这些运动信号往往不可观测。此外，这些场景中的序贯决策行为可能偏离标准马尔可夫决策过程（MDP）的假设。为应对这些挑战，我们探索了基于非马尔可夫决策过程（nMDP）的纯状态序列深度生成建模方法，其中策略作为状态转移生成器潜在空间中的能量基先验。我们通过最大似然估计实现基于模型的模仿，该方法涉及从先验分布进行短程MCMC采样以及后验分布的重要性采样。学习到的模型能够实现"作为推论的决策"：无模型策略执行等价于先验采样，而基于模型的规划则是以策略初始化的后验采样。我们在具有非马尔可夫约束的典型路径规划任务中验证了所提方法的有效性，并证明该模型在MuJoCo基准套件的挑战性领域中展现出强劲性能。