A Robust Policy Bootstrapping Algorithm for Multi-objective Reinforcement Learning in Non-stationary Environments

TL;DR

This paper presents a new multi-objective reinforcement learning algorithm that adaptively evolves policy coverage sets in non-stationary environments, outperforming existing methods in dynamic settings.

Contribution

It introduces a robust, online developmental optimization algorithm for evolving policy coverage sets in non-stationary environments, addressing a key limitation of prior methods.

Findings

Outperforms existing algorithms in non-stationary environments

Achieves comparable results to state-of-the-art in stationary environments

Demonstrates robustness and adaptability in dynamic settings

Abstract

Multi-objective Markov decision processes are a special kind of multi-objective optimization problem that involves sequential decision making while satisfying the Markov property of stochastic processes. Multi-objective reinforcement learning methods address this problem by fusing the reinforcement learning paradigm with multi-objective optimization techniques. One major drawback of these methods is the lack of adaptability to non-stationary dynamics in the environment. This is because they adopt optimization procedures that assume stationarity to evolve a coverage set of policies that can solve the problem. This paper introduces a developmental optimization approach that can evolve the policy coverage set while exploring the preference space over the defined objectives in an online manner. We propose a novel multi-objective reinforcement learning algorithm that can robustly evolve a convex coverage set of policies in an online manner in non-stationary environments. We compare the proposed algorithm with two state-of-the-art multi-objective reinforcement learning algorithms in stationary and non-stationary environments. Results showed that the proposed algorithm significantly outperforms the existing algorithms in non-stationary environments while achieving comparable results in stationary environments.