mlsanityData to 14 Oct 2026

Genes in Intelligent Agents

arXiv 2306.10225 ↗First posted 17 Jun 2023cs.NE cs.AI cs.LGcited by 9

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Abstract (arXiv)

The genes in nature give the lives on earth the current biological intelligence through transmission and accumulation over billions of years. Inspired by the biological intelligence, artificial intelligence (AI) has devoted to building the machine intelligence. Although it has achieved thriving successes, the machine intelligence still lags far behind the biological intelligence. The reason may lie in that animals are born with some intelligence encoded in their genes, but machines lack such intelligence and learn from scratch. Inspired by the genes of animals, we define the ``genes'' of machines named as the ``learngenes'' and propose the Genetic Reinforcement Learning (GRL). GRL is a computational framework that simulates the evolution of organisms in reinforcement learning (RL) and leverages the learngenes to learn and evolve the intelligence agents. Leveraging GRL, we first show that the learngenes take the form of the fragments of the agents' neural networks and can be inherited across generations. Second, we validate that the learngenes can transfer ancestral experience to the agents and bring them instincts and strong learning abilities. Third, we justify the Lamarckian inheritance of the intelligent agents and the continuous evolution of the learngenes. Overall, the learngenes have taken the machine intelligence one more step toward the biological intelligence.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 17 of its 78 references that are in the map. First posted on arXiv 17 Jun 2023.

Builds on

17 of 78 references.

References · 17

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
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  9. 2018Deep Reinforcement Learning for Multi-Agent Systems: A Review of Challenges, Solutions and Applicationscited by 127
  10. 2022Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolutioncited by 78
  11. 2022Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progresscited by 65
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5 more
  1. 2021Brain-inspired computing: We need a master plancited by 51
  2. 2021Deep Reinforcement Learning in Computer Vision: A Comprehensive Surveycited by 43
  3. 2021No-Press Diplomacy from Scratchcited by 31
  4. 2022RLx2: Training a Sparse Deep Reinforcement Learning Model from Scratchcited by 30
  5. 2021Learngene: From Open-World to Your Learning Taskcited by 27

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 5; 2025: 3; 2026: 1.

9 papers.

  1. 2024WAVE: Weight Templates for Adaptive Initialization of Variable-sized Modelscited by 17
  2. 2024Transferring Core Knowledge via Learngenescited by 10
  3. 2024KIND: Knowledge Integration and Diversion for Training Decomposable Modelscited by 10
  4. 2025DivControl: Knowledge Diversion for Controllable Image Generationcited by 7
  5. 2024FINE: Factorizing Knowledge for Initialization of Variable-sized Diffusion Modelscited by 5
  6. 2024SVasP: Self-Versatility Adversarial Style Perturbation for Cross-Domain Few-Shot Learningcited by 5
  7. 2025Distribution-Conditional Generation: From Class Distribution to Creative Generationcited by 4
  8. 2025FAD: Frequency Adaptation and Diversion for Cross-domain Few-shot Learningcited by 1
  9. 2026SRasP: Self-Reorientation Adversarial Style Perturbation for Cross-Domain Few-Shot Learningcited by 0