A Variance Analysis for POMDP Policy Evaluation.

Area of Science:

• Artificial Intelligence
• Robotics
• Medical Decision Making

Background:

• Partially Observable Markov Decision Processes (POMDPs) are widely used for decision-making under uncertainty.
• Calculating value functions for policies in POMDPs requires accurate transition and observation models.
• Empirical models derived from data introduce bias and variance into value function estimations.

Purpose of the Study:

• To propose a method for estimating the bias and variance of value functions in POMDPs.
• To enable meaningful comparison of different policies by quantifying estimation errors.
• To provide formal guarantees on the quality of implemented policies.

Main Methods:

• Estimating value function bias and variance using statistics of empirical transition and observation models.
• Developing a method applicable to learned models from on-policy trajectories.
• Utilizing experimental validation in robotics and medical decision-making domains.

Main Results:

• A method to quantify the uncertainty associated with value function estimates.
• Demonstration of how bias and variance estimation aids policy comparison.
• Validation of the proposed method's precision through empirical studies.

Conclusions:

• The proposed method offers a way to formally assess policy quality in POMDPs.
• Accurate bias and variance estimation is crucial for robust sequential decision-making.
• The approach has practical implications for fields like robotics and healthcare.