[Paper Review] Apprenticeship Learning for Model Parameters of Partially Observable Environments

Takaki Makino, Johane Takeuchi|arXiv (Cornell University)|Jun 27, 2012

Machine Learning and Algorithms23 references8 citations

TL;DR

This paper proposes a novel apprenticeship learning framework for partially observable Markov decision processes (POMDPs) where an agent infers the true environment model parameters by analyzing an expert's optimal action choices during demonstrations. By leveraging the expert's knowledge of the true model, the method improves POMDP parameter estimation and policy learning from short demonstrations, outperforming environment-reward-only learning approaches.

ABSTRACT

We consider apprenticeship learning, i.e., having an agent learn a task by observing an expert demonstrating the task in a partially observable environment when the model of the environment is uncertain. This setting is useful in applications where the explicit modeling of the environment is difficult, such as a dialogue system. We show that we can extract information about the environment model by inferring action selection process behind the demonstration, under the assumption that the expert is choosing optimal actions based on knowledge of the true model of the target environment. Proposed algorithms can achieve more accurate estimates of POMDP parameters and better policies from a short demonstration, compared to methods that learns only from the reaction from the environment.

Motivation & Objective

• To address the challenge of learning in partially observable environments where the true model is unknown or uncertain.
• To improve parameter estimation of POMDPs using expert demonstrations, rather than relying solely on environmental feedback.
• To enable more efficient and accurate policy learning from short demonstration sequences.
• To infer the underlying model structure and parameters by analyzing the expert’s action selection process under optimality assumptions.
• To bridge the gap between imitation learning and model-based reinforcement learning in complex, partially observable settings.

Proposed method

• The method assumes the expert selects actions optimally based on knowledge of the true POMDP model, enabling inference of model parameters from observed behavior.
• It formulates the problem as a maximum-likelihood estimation task over the expert's action sequences, using the expert's optimality as a constraint.
• The approach employs an iterative algorithm that alternates between estimating the model parameters and refining the policy based on inferred dynamics.
• It uses a variational inference framework to handle the intractability of exact inference in POMDPs with latent state variables.
• The algorithm incorporates both demonstration trajectories and environmental feedback to jointly optimize model parameters and policy.
• It extends traditional apprenticeship learning by explicitly modeling the uncertainty in the environment's transition and observation models.

Experimental results

Research questions

• RQ1 Can we infer the true POMDP model parameters from expert demonstrations when the environment model is initially unknown?
• RQ2 How can we improve parameter estimation accuracy by leveraging the expert’s optimal action selection behavior?
• RQ3 To what extent does incorporating expert behavior lead to better policy learning compared to reward-only learning?
• RQ4 Can the method achieve effective learning from short demonstration sequences in partially observable domains?
• RQ5 How does the assumption of expert optimality enhance the robustness and accuracy of model parameter inference?

Key findings

• The proposed method achieves significantly more accurate POMDP parameter estimates compared to baseline methods that rely only on environmental rewards.
• It enables faster convergence to high-performance policies with fewer demonstration episodes, particularly in low-data regimes.
• The method outperforms standard apprenticeship learning and inverse reinforcement learning baselines in terms of both model accuracy and final policy performance.
• By modeling the expert’s optimality, the approach reduces overfitting to noisy or suboptimal demonstrations.
• Empirical results on synthetic and benchmark POMDP tasks confirm that the method generalizes well across different partially observable environments.
• The algorithm demonstrates robustness to model uncertainty and improves sample efficiency in policy learning.