Utilizing Novelty-based Evolution Strategies to Train Transformers in Reinforcement Learning
Fuente:
arXiv
Guardado en:
| Autores principales: | Lorenc, Matyáš, Neruda, Roman |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Utilizing Evolution Strategies to Train Transformers in Reinforcement Learning
por: Lorenc, Matyáš, et al.
Publicado: (2025)
por: Lorenc, Matyáš, et al.
Publicado: (2025)
Hard-Thresholding Meets Evolution Strategies in Reinforcement Learning
por: Gao, Chengqian, et al.
Publicado: (2024)
por: Gao, Chengqian, et al.
Publicado: (2024)
Reinforcement Learning-based Self-adaptive Differential Evolution through Automated Landscape Feature Learning
por: Guo, Hongshu, et al.
Publicado: (2025)
por: Guo, Hongshu, et al.
Publicado: (2025)
EvIL: Evolution Strategies for Generalisable Imitation Learning
por: Sapora, Silvia, et al.
Publicado: (2024)
por: Sapora, Silvia, et al.
Publicado: (2024)
Efficient Multi-Objective Neural Architecture Search via Pareto Dominance-based Novelty Search
por: Vo, An, et al.
Publicado: (2024)
por: Vo, An, et al.
Publicado: (2024)
Dominated Novelty Search: Rethinking Local Competition in Quality-Diversity
por: Bahlous-Boldi, Ryan, et al.
Publicado: (2025)
por: Bahlous-Boldi, Ryan, et al.
Publicado: (2025)
Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning
por: Qiu, Xin, et al.
Publicado: (2025)
por: Qiu, Xin, et al.
Publicado: (2025)
Differential Evolution Algorithm based Hyper-Parameters Selection of Transformer Neural Network Model for Load Forecasting
por: Sen, Anuvab, et al.
Publicado: (2023)
por: Sen, Anuvab, et al.
Publicado: (2023)
Non-linear PCA via Evolution Strategies: a Novel Objective Function
por: Uriot, Thomas, et al.
Publicado: (2026)
por: Uriot, Thomas, et al.
Publicado: (2026)
The Evolution of Learning Algorithms for Artificial Neural Networks
por: Baxter, Jonathan
Publicado: (2025)
por: Baxter, Jonathan
Publicado: (2025)
Gradient-Free Training of Spiking Neural Networks via Low-Rank Evolution Strategies
por: Patankar, Dhruv, et al.
Publicado: (2026)
por: Patankar, Dhruv, et al.
Publicado: (2026)
Looped Transformers are Better at Learning Learning Algorithms
por: Yang, Liu, et al.
Publicado: (2023)
por: Yang, Liu, et al.
Publicado: (2023)
Large Language Models for Tuning Evolution Strategies
por: Kramer, Oliver
Publicado: (2024)
por: Kramer, Oliver
Publicado: (2024)
Multi-Class Imbalanced Learning with Support Vector Machines via Differential Evolution
por: Zhang, Zhong-Liang, et al.
Publicado: (2025)
por: Zhang, Zhong-Liang, et al.
Publicado: (2025)
Stein Variational Evolution Strategies
por: Braun, Cornelius V., et al.
Publicado: (2024)
por: Braun, Cornelius V., et al.
Publicado: (2024)
Synergizing Reinforcement Learning and Genetic Algorithms for Neural Combinatorial Optimization
por: Gu, Shengda, et al.
Publicado: (2025)
por: Gu, Shengda, et al.
Publicado: (2025)
Reinforcement Learning-assisted Constraint Relaxation for Constrained Expensive Optimization
por: Zhu, Qianhao, et al.
Publicado: (2026)
por: Zhu, Qianhao, et al.
Publicado: (2026)
Evolutionary Warm-Starts for Reinforcement Learning in Industrial Continuous Control
por: Maus, Tom, et al.
Publicado: (2026)
por: Maus, Tom, et al.
Publicado: (2026)
On the Importance of Reward Design in Reinforcement Learning-based Dynamic Algorithm Configuration: A Case Study on OneMax with (1+($λ$,$λ$))-GA
por: Nguyen, Tai, et al.
Publicado: (2025)
por: Nguyen, Tai, et al.
Publicado: (2025)
Combining Neuroevolution with the Search for Novelty to Improve the Generation of Test Inputs for Games
por: Feldmeier, Patric, et al.
Publicado: (2024)
por: Feldmeier, Patric, et al.
Publicado: (2024)
Generalized Population-Based Training for Hyperparameter Optimization in Reinforcement Learning
por: Bai, Hui, et al.
Publicado: (2024)
por: Bai, Hui, et al.
Publicado: (2024)
Learning Where, What and How to Transfer: A Multi-Role Reinforcement Learning Approach for Evolutionary Multitasking
por: Zhan, Jiajun, et al.
Publicado: (2025)
por: Zhan, Jiajun, et al.
Publicado: (2025)
Directly Learning Stock Trading Strategies Through Profit Guided Loss Functions
por: Kar, Devroop, et al.
Publicado: (2025)
por: Kar, Devroop, et al.
Publicado: (2025)
Large Language Models As Evolution Strategies
por: Lange, Robert Tjarko, et al.
Publicado: (2024)
por: Lange, Robert Tjarko, et al.
Publicado: (2024)
Transforming Datasets to Requested Complexity with Projection-based Many-Objective Genetic Algorithm
por: Komorniczak, Joanna
Publicado: (2025)
por: Komorniczak, Joanna
Publicado: (2025)
Advancing Training Efficiency of Deep Spiking Neural Networks through Rate-based Backpropagation
por: Yu, Chengting, et al.
Publicado: (2024)
por: Yu, Chengting, et al.
Publicado: (2024)
ConfigX: Modular Configuration for Evolutionary Algorithms via Multitask Reinforcement Learning
por: Guo, Hongshu, et al.
Publicado: (2024)
por: Guo, Hongshu, et al.
Publicado: (2024)
Improving Algorithm-Selection and Performance-Prediction via Learning Discriminating Training Samples
por: Renau, Quentin, et al.
Publicado: (2024)
por: Renau, Quentin, et al.
Publicado: (2024)
The Stacked Autoencoder Evolution Hypothesis
por: Iizuka, Hiroyuki
Publicado: (2026)
por: Iizuka, Hiroyuki
Publicado: (2026)
CGP++ : A Modern C++ Implementation of Cartesian Genetic Programming
por: Kalkreuth, Roman, et al.
Publicado: (2024)
por: Kalkreuth, Roman, et al.
Publicado: (2024)
State-Space Constraints Can Improve the Generalisation of the Differentiable Neural Computer to Input Sequences With Unseen Length
por: Ofner, Patrick, et al.
Publicado: (2021)
por: Ofner, Patrick, et al.
Publicado: (2021)
Financial Decision Making using Reinforcement Learning with Dirichlet Priors and Quantum-Inspired Genetic Optimization
por: Nandy, Prasun, et al.
Publicado: (2025)
por: Nandy, Prasun, et al.
Publicado: (2025)
Bullet Trains: Parallelizing Training of Temporally Precise Spiking Neural Networks
por: Morrill, Todd, et al.
Publicado: (2026)
por: Morrill, Todd, et al.
Publicado: (2026)
Multi-parameter Control for the $(1+(λ,λ))$-GA on OneMax via Deep Reinforcement Learning
por: Nguyen, Tai, et al.
Publicado: (2025)
por: Nguyen, Tai, et al.
Publicado: (2025)
An Approach to Analyze Niche Evolution in XCS Models
por: Lanzi, Pier Luca
Publicado: (2025)
por: Lanzi, Pier Luca
Publicado: (2025)
Foxtsage vs. Adam: Revolution or Evolution in Optimization?
por: Aula, Sirwan A., et al.
Publicado: (2024)
por: Aula, Sirwan A., et al.
Publicado: (2024)
CDRL: A Reinforcement Learning Framework Inspired by Cerebellar Circuits and Dendritic Computational Strategies
por: Zhang, Sibo, et al.
Publicado: (2026)
por: Zhang, Sibo, et al.
Publicado: (2026)
Multiple Population Alternate Evolution Neural Architecture Search
por: Zou, Juan, et al.
Publicado: (2024)
por: Zou, Juan, et al.
Publicado: (2024)
Investigating Quantum Circuit Designs Using Neuro-Evolution
por: Kar, Devroop, et al.
Publicado: (2026)
por: Kar, Devroop, et al.
Publicado: (2026)
Guided Evolution with Binary Discriminators for ML Program Search
por: Co-Reyes, John D., et al.
Publicado: (2024)
por: Co-Reyes, John D., et al.
Publicado: (2024)
Ejemplares similares
-
Utilizing Evolution Strategies to Train Transformers in Reinforcement Learning
por: Lorenc, Matyáš, et al.
Publicado: (2025) -
Hard-Thresholding Meets Evolution Strategies in Reinforcement Learning
por: Gao, Chengqian, et al.
Publicado: (2024) -
Reinforcement Learning-based Self-adaptive Differential Evolution through Automated Landscape Feature Learning
por: Guo, Hongshu, et al.
Publicado: (2025) -
EvIL: Evolution Strategies for Generalisable Imitation Learning
por: Sapora, Silvia, et al.
Publicado: (2024) -
Efficient Multi-Objective Neural Architecture Search via Pareto Dominance-based Novelty Search
por: Vo, An, et al.
Publicado: (2024)