Defining Problem from Solutions: Inverse Reinforcement Learning (IRL) and Its Applications for Next-Generation Networking

TL;DR

This paper addresses reward-design challenges in Deep Reinforcement Learning for Next-Generation Networking by advocating Inverse Reinforcement Learning (IRL) as a mechanism to infer rewards from expert demonstrations. It provides a comprehensive overview of IRL fundamentals, contrasts IRL with DRL, surveys NGN applications, and presents a case study on human-centered prompt engineering in Generative AI-enabled networks, where IRL achieves superior alignment with human preferences. The results show IRL can infer unobserved rewards and guide policy optimization in complex, human-in-the-loop NGN environments, outperforming DRL in key metrics. The work outlines promising directions such as mixture-of-experts, enhanced human feedback, and security safeguards to enable robust, human-aligned IRL deployment in NGN.

Abstract

Performance optimization is a critical concern in networking, on which Deep Reinforcement Learning (DRL) has achieved great success. Nonetheless, DRL training relies on precisely defined reward functions, which formulate the optimization objective and indicate the positive/negative progress towards the optimal. With the ever-increasing environmental complexity and human participation in Next-Generation Networking (NGN), defining appropriate reward functions become challenging. In this article, we explore the applications of Inverse Reinforcement Learning (IRL) in NGN. Particularly, if DRL aims to find optimal solutions to the problem, IRL finds a problem from the optimal solutions, where the optimal solutions are collected from experts, and the problem is defined by reward inference. Specifically, we first formally introduce the IRL technique, including its fundamentals, workflow, and difference from DRL. Afterward, we present the motivations of IRL applications in NGN and survey existing studies. Furthermore, to demonstrate the process of applying IRL in NGN, we perform a case study about human-centric prompt engineering in Generative AI-enabled networks. We demonstrate the effectiveness of using both DRL and IRL techniques and prove the superiority of IRL.

Figures (5)

• Figure 1: The fundamentals of IRL (a): The cases of defining reward functions. (b): The illustration of MDP. (c): The workflow of DRL algorithm. (d) The workflow of IRL. Note that we illustrate four representative approaches to infer reward, namely maximum margin, maximum entropy, GAIL, and RLHF.
• Figure 2: The motivation of applying IRL in NGN, using QoE optimization for task offloading as an example. A: The attackers' information is hidden from users. B: Subjectivity factors can hardly be represented precisely. C: The immediate reward of each action is unobserved. Action 1 and 1' choose servers $H$ and $G$ for task offloading, respectively. D: SAGIN communications involve numerous physical factors from diverse devices, causing huge difficulty in QoE modeling. E: The human driving behaviors contain complicated physiological processes that can hardly be described by manually designed rewards.
• Figure 3: The illustration of our case study. A: The system model of GAI-enabled network. B: The efficacy of prompt engineering. We can observe that the image generated from the crafted prompt excels in object rendering and image composition. C: The illustration of DRL-based prompt engineering. D: The illustration of IRL-based prompt engineering.
• Figure 4: The training curves of DRL and IRL.
• Figure 5: The efficacy of DRL and IRL in prompt engineering. Each case corresponds to one randomly selected raw prompt.