Pareto Q-Learning with Reward Machines

2026-06-17 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

Pareto Q-Learning with Reward Machines (PQLRM) is a new multi-objective reinforcement learning algorithm designed for tasks where reward structures are defined by reward machines (RMs). PQLRM integrates Pareto Q-Learning (PQL), which uses vector-valued Q-estimates to approximate the Pareto front, with enhancements from Q-Learning with Reward Machines (QRM), which leverages the factored automaton structure of the reward signal. This combination results in a multi-policy algorithm that maintains sample efficiency even with non-Markovian, RM-encoded rewards. Experimental trials demonstrate that PQLRM achieves faster convergence compared to a naive PQL baseline when applied to a cross-product Markov Decision Process (MDP). Furthermore, PQLRM can synthesize Pareto-optimal policies that QRM alone is unable to generate. The algorithm was published on 2026-06-17.

Key takeaway

For AI scientists designing multi-objective reinforcement learning systems, PQLRM offers a robust approach for tasks with complex, non-Markovian reward structures. You should consider integrating reward machines to define your reward signals. PQLRM demonstrates faster convergence and synthesizes Pareto-optimal policies that traditional QRM cannot. This method could significantly improve the efficiency and policy breadth of your MORL applications.

Key insights

PQLRM combines PQL and QRM to efficiently learn multi-objective, non-Markovian policies using reward machines.

Principles

• Exploiting factored reward structures enhances MORL.
• Combining multi-objective and RM-based Q-learning improves efficiency.
• Pareto Q-Learning can synthesize policies beyond single-objective methods.

Method

PQLRM integrates Pareto Q-Learning's vector-valued Q-estimates with QRM's exploitation of reward machine automaton structures to approximate Pareto fronts and learn multi-policies.

In practice

• Apply PQLRM for complex multi-objective RL tasks.
• Use reward machines to define non-Markovian reward structures.
• Consider PQLRM for faster convergence in MORL.

Topics

• Multi-objective Reinforcement Learning
• Pareto Q-Learning
• Reward Machines
• Q-Learning with Reward Machines
• Non-Markovian Rewards
• Policy Synthesis

Best for: Research Scientist, AI Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.