mlsanityData to 14 Oct 2026

Design Optimization of Nuclear Fusion Reactor through Deep Reinforcement Learning

arXiv 2409.08231 ↗First posted 12 Sep 2024physics.plasm-ph cs.AI cs.LGcited by 1

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

This research explores the application of Deep Reinforcement Learning (DRL) to optimize the design of a nuclear fusion reactor. DRL can efficiently address the challenging issues attributed to multiple physics and engineering constraints for steady-state operation. The fusion reactor design computation and the optimization code applicable to parallelization with DRL are developed. The proposed framework enables finding the optimal reactor design that satisfies the operational requirements while reducing building costs. Multi-objective design optimization for a fusion reactor is now simplified by DRL, indicating the high potential of the proposed framework for advancing the efficient and sustainable design of future reactors.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 7 of its 42 references that are in the map. First posted on arXiv 12 Sep 2024.

Builds on

7 of 42 references.

References · 7

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2015Trust Region Policy Optimizationcited by 2,580
  3. 2017Reinforcement Learning with Deep Energy-Based Policiescited by 669
  4. 2024Reinforcement learningcited by 247
  5. 2017Efficient Parallel Methods for Deep Reinforcement Learningcited by 32
  6. 2019Direct shape optimization through deep reinforcement learningcited by 28
  7. 2019Scalings for Tokamak Energy Confinementcited by 3

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025A Digital Twin Framework for Generation-IV Reactors with Reinforcement Learning-Enabled Health-Aware Supervisory Controlcited by 0

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  3. 2024Multistep Criticality Search and Power Shaping in Microreactors with Reinforcement Learning
  4. 2026Offline Reinforcement Learning for Plasma Control in Nuclear Fusion: Codebase and Benchmark
  5. 2024SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning
  6. 2025Autonomous Pressure Control in MuVacAS via Deep Reinforcement Learning and Deep Learning Surrogate Models
  7. 2026Techno-economic optimization of a heat-pipe microreactor, part II: multi-objective optimization analysis
  8. 2024Harnessing the Power of Gradient-Based Simulations for Multi-Objective Optimization in Particle Accelerators