MetaCURL: Non-stationary Concave Utility Reinforcement Learning

TL;DR

MetaCURL is a novel algorithm designed for non-stationary Markov decision processes, enabling effective reinforcement learning with convex performance criteria despite environment changes and partial information.

Contribution

It introduces MetaCURL, the first CURL algorithm for non-stationary MDPs, using a meta-algorithm with multiple instances and a sleeping expert framework to handle environment changes.

Findings

Achieves optimal dynamic regret without prior knowledge of MDP changes.

Handles full adversarial losses, not just stochastic environments.

Effectively manages non-stationarity with experts in partial information settings.

Abstract

We explore online learning in episodic loop-free Markov decision processes on non-stationary environments (changing losses and probability transitions). Our focus is on the Concave Utility Reinforcement Learning problem (CURL), an extension of classical RL for handling convex performance criteria in state-action distributions induced by agent policies. While various machine learning problems can be written as CURL, its non-linearity invalidates traditional Bellman equations. Despite recent solutions to classical CURL, none address non-stationary MDPs. This paper introduces MetaCURL, the first CURL algorithm for non-stationary MDPs. It employs a meta-algorithm running multiple black-box algorithms instances over different intervals, aggregating outputs via a sleeping expert framework. The key hurdle is partial information due to MDP uncertainty. Under partial information on the probability transitions (uncertainty and non-stationarity coming only from external noise, independent of agent state-action pairs), we achieve optimal dynamic regret without prior knowledge of MDP changes. Unlike approaches for RL, MetaCURL handles full adversarial losses, not just stochastic ones. We believe our approach for managing non-stationarity with experts can be of interest to the RL community.