An Offline Risk-aware Policy Selection Method for Bayesian Markov Decision Processes

TL;DR

This paper introduces EvC, a Bayesian-based offline policy selection method that balances risk and robustness, effectively choosing policies that perform reliably in real-world applications despite limited data.

Contribution

The paper proposes a novel risk-aware policy selection framework, EvC, that incorporates Bayesian uncertainty to improve robustness in offline MDP planning and reinforcement learning.

Findings

EvC effectively selects robust policies in simple discrete environments.

EvC outperforms state-of-the-art approaches in terms of robustness.

The method is suitable for offline applications prioritizing safety and reliability.

Abstract

In Offline Model Learning for Planning and in Offline Reinforcement Learning, the limited data set hinders the estimate of the Value function of the relative Markov Decision Process (MDP). Consequently, the performance of the obtained policy in the real world is bounded and possibly risky, especially when the deployment of a wrong policy can lead to catastrophic consequences. For this reason, several pathways are being followed with the scope of reducing the model error (or the distributional shift between the learned model and the true one) and, more broadly, obtaining risk-aware solutions with respect to model uncertainty. But when it comes to the final application which baseline should a practitioner choose? In an offline context where computational time is not an issue and robustness is the priority we propose Exploitation vs Caution (EvC), a paradigm that (1) elegantly incorporates model uncertainty abiding by the Bayesian formalism, and (2) selects the policy that maximizes a risk-aware objective over the Bayesian posterior between a fixed set of candidate policies provided, for instance, by the current baselines. We validate EvC with state-of-the-art approaches in different discrete, yet simple, environments offering a fair variety of MDP classes. In the tested scenarios EvC manages to select robust policies and hence stands out as a useful tool for practitioners that aim to apply offline planning and reinforcement learning solvers in the real world.