Robust Constrained-MDPs: Soft-Constrained Robust Policy Optimization under Model Uncertainty

TL;DR

This paper introduces robust constrained Markov decision processes (RCMDPs) to enhance reinforcement learning algorithms with performance and safety guarantees under model uncertainty, especially useful for real-world applications like Sim2Real transfer.

Contribution

The paper merges CMDP and RMDP theories to formulate RCMDPs, enabling the design of robust RL algorithms with constraint satisfaction guarantees under model uncertainty.

Findings

Proposed a Lagrangian-based robust policy gradient algorithm.

Validated the approach on an inventory management problem.

Demonstrated robustness and safety guarantees in uncertain environments.

Abstract

In this paper, we focus on the problem of robustifying reinforcement learning (RL) algorithms with respect to model uncertainties. Indeed, in the framework of model-based RL, we propose to merge the theory of constrained Markov decision process (CMDP), with the theory of robust Markov decision process (RMDP), leading to a formulation of robust constrained-MDPs (RCMDP). This formulation, simple in essence, allows us to design RL algorithms that are robust in performance, and provides constraint satisfaction guarantees, with respect to uncertainties in the system's states transition probabilities. The need for RCMPDs is important for real-life applications of RL. For instance, such formulation can play an important role for policy transfer from simulation to real world (Sim2Real) in safety critical applications, which would benefit from performance and safety guarantees which are robust w.r.t model uncertainty. We first propose the general problem formulation under the concept of RCMDP, and then propose a Lagrangian formulation of the optimal problem, leading to a robust-constrained policy gradient RL algorithm. We finally validate this concept on the inventory management problem.