mlsanityData to 7 Oct 2026

Communication Load Balancing via Efficient Inverse Reinforcement Learning

arXiv 2303.16686 ↗First posted 22 Mar 2023cs.NI cs.AI cs.LGcited by 0

On the map: Offline RL and imitation learning from data → Inverse reinforcement learning

Abstract (arXiv)

Communication load balancing aims to balance the load between different available resources, and thus improve the quality of service for network systems. After formulating the load balancing (LB) as a Markov decision process problem, reinforcement learning (RL) has recently proven effective in addressing the LB problem. To leverage the benefits of classical RL for load balancing, however, we need an explicit reward definition. Engineering this reward function is challenging, because it involves the need for expert knowledge and there lacks a general consensus on the form of an optimal reward function. In this work, we tackle the communication load balancing problem from an inverse reinforcement learning (IRL) approach. To the best of our knowledge, this is the first time IRL has been successfully applied in the field of communication load balancing. Specifically, first, we infer a reward function from a set of demonstrations, and then learn a reinforcement learning load balancing policy with the inferred reward function. Compared to classical RL-based solution, the proposed solution can be more general and more suitable for real-world scenarios. Experimental evaluations implemented on different simulated traffic scenarios have shown our method to be effective and better than other baselines by a considerable margin.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Inverse reinforcement learning, one of 13 directions in Offline RL and imitation learning from data. Builds on 9 of its 39 references that are in the map. First posted on arXiv 22 Mar 2023.

Builds on

9 of 39 references.

Closest ancestors · 1

Same direction, newest first

  1. 2019Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations12 Apr

Other references · 8

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2017Proximal Policy Optimization Algorithmscited by 14,441
  3. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  4. 2016Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimizationcited by 400
  5. 2017Deep Reinforcement Learning framework for Autonomous Drivingcited by 224
  6. 2021Learning Variable Impedance Control via Inverse Reinforcement Learning for Force-Related Taskscited by 10
  7. 2021ModelLight: Model-Based Meta-Reinforcement Learning for Traffic Signal Controlcited by 8
  8. 2022Meta-Reinforcement Learning for Building Energy Management Systemcited by 4

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  1. 2024Towards Sample-Efficiency and Generalization of Transfer and Inverse Reinforcement Learning: A Comprehensive Literature Review
  2. 2018A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress
  3. 2024How does Inverse RL Scale to Large State Spaces? A Provably Efficient Approach
  4. 2023Offline Reinforcement Learning for Wireless Network Optimization with Mixture Datasets
  5. 2023Is Inverse Reinforcement Learning Harder than Standard Reinforcement Learning? A Theoretical Perspective
  6. 2026Inverse Reinforcement Learning without an Optimal Demonstrator: A Feasible Reward Set Approach
  7. 2026ReLibra: Routing-Replay-Guided Load Balancing for MoE Training in Reinforcement Learning
  8. 2025Reward Compatibility: A Framework for Inverse RL