# RL for energy, networks and operations

Area: [Deep reinforcement learning methods](https://mlsanity.com/deeprl/index.md). HTML: https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/

## Key facts

- RL for energy, networks and operations: 837 papers on arXiv since 2024 (data through 14 Oct 2026).
- 13 of its 837 mapped papers (2%) cite “Finite-Time Analysis of On-Policy Heterogeneous Federated Reinforcement Learning” (2024, arXiv 2401.15273).
- 7 of its 837 mapped papers (1%) cite “Reinforcement Learning-based Model Predictive Control for Greenhouse Climate Control” (2024, arXiv 2409.12789).
- 12 new papers 30 Sep–14 Oct.

## Most cited here

- [Finite-Time Analysis of On-Policy Heterogeneous Federated Reinforcement Learning](https://mlsanity.com/paper/2401.15273/) (2024), cited by 13 papers here
- [Reinforcement Learning-based Model Predictive Control for Greenhouse Climate Control](https://mlsanity.com/paper/2409.12789/) (2024), cited by 7 papers here
- [HUGO -- Highlighting Unseen Grid Options: Combining Deep Reinforcement Learning with a Heuristic Target Topology Approach](https://mlsanity.com/paper/2405.00629/) (2024), cited by 6 papers here
- [CityLearn v2: Energy-flexible, resilient, occupant-centric, and carbon-aware management of grid-interactive communities](https://mlsanity.com/paper/2405.03848/) (2024), cited by 6 papers here
- [The Sample-Communication Complexity Trade-off in Federated Q-Learning](https://mlsanity.com/paper/2408.16981/) (2024), cited by 5 papers here
- [Global Convergence Guarantees for Federated Policy Gradient Methods with Adversaries](https://mlsanity.com/paper/2403.09940/) (2024), cited by 4 papers here
- [Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis](https://mlsanity.com/paper/2404.08003/) (2024), cited by 4 papers here
- [Control-Informed Reinforcement Learning for Chemical Processes](https://mlsanity.com/paper/2408.13566/) (2024), cited by 4 papers here
- [Optimizing Power Grid Topologies with Reinforcement Learning: A Survey of Methods and Challenges](https://mlsanity.com/paper/2504.08210/) (2025), cited by 4 papers here
- [Leveraging Digital Cousins for Ensemble Q-Learning in Large-Scale Wireless Networks](https://mlsanity.com/paper/2402.08022/) (2024), cited by 3 papers here

## New (30 Sep–14 Oct)

- [Deep Reinforcement Learning for HPC Job Scheduling: A Statistical Evaluation](https://mlsanity.com/paper/2610.21008/) (2026-10-14)
- [Design Space Exploration of Reinforcement Learning Configurations for Post-Legalization Placement](https://mlsanity.com/paper/2610.20224/) (2026-10-14)
- [Learning When to Transfer: Reinforcement Learning for Carbon-Footprint Modeling in New Manufacturing Domains](https://mlsanity.com/paper/2610.18730/) (2026-10-13)
- [NetRL: Powering Network Decisions via NetGRPO-Driven Reinforcement Learning](https://mlsanity.com/paper/2610.16226/) (2026-10-12)
- [Game-Theoretic Reinforcement Learning based Topology-Agnostic Defense for Virtual Power Plants](https://mlsanity.com/paper/2610.15183/) (2026-10-10)
- [Learning to Orchestrate Evolutionary Search: Progression-Aware Deep Reinforcement Learning for Dynamic DE-CMA-ES Coordination in Optimization and Structural Model Updating](https://mlsanity.com/paper/2610.11546/) (2026-10-08)
- [Strategic Investment Decision Making for Value Creation in Energy Transition: A Reinforcement Learning Approach](https://mlsanity.com/paper/2610.10768/) (2026-10-07)
- [GPlaceRL: An Open-Source Graph Reinforcement Learning Framework for Detailed Placement](https://mlsanity.com/paper/2610.06489/) (2026-10-05)
- [CRAFTER: Causality-based Self-adaptation for Autonomous IoT Systems](https://mlsanity.com/paper/2610.06320/) (2026-10-05)
- [RollPlace: Improving Macro Placement via Monte Carlo Rollout Search](https://mlsanity.com/paper/2610.06316/) (2026-10-05)
- [Reinforcement Learning Techniques for the Optimization of Target Polarization in Nuclear Physics Scattering Experiments](https://mlsanity.com/paper/2610.02452/) (2026-10-01)
- [Reinforcement Learning-Guided Graph Transformations for SpTRSV Optimization](https://mlsanity.com/paper/2609.40159/) (2026-09-30)