Imagen de Portada

A Temporal Difference Method for Stochastic Continuous Dynamics

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Settai, Haruki, Takeishi, Naoya, Yairi, Takehisa
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Forecast Sports Outcomes under Efficient Market Hypothesis: Theoretical and Experimental Analysis of Odds-Only and Generalised Linear Models
por: Goto, Kaito, et al.
Publicado: (2026)

Learning Visually Interpretable Oscillator Networks for Soft Continuum Robots from Video
por: Krauss, Henrik, et al.
Publicado: (2025)

Estimating Central, Peripheral, and Temporal Visual Contributions to Human Decision Making in Atari Games
por: Krauss, Henrik, et al.
Publicado: (2026)

Revealing Human Attention Patterns from Gameplay Analysis for Reinforcement Learning
por: Krauss, Henrik, et al.
Publicado: (2025)

UniVer: A Unified Perspective for Multi-step and Multi-draft Speculative Decoding
por: Weng, Yepeng, et al.
Publicado: (2026)

Deterministic Decomposition of Stochastic Generative Dynamics
por: Song, Xingyu, et al.
Publicado: (2026)

Accurate Open-Loop Control of a Soft Continuum Robot Through Visually Learned Latent Representations
por: Krauss, Henrik, et al.
Publicado: (2026)

Kolmogorov-Smirnov GAN
por: Falkiewicz, Maciej, et al.
Publicado: (2024)

Simulation-Efficient Cosmological Inference with Multi-Fidelity SBI
por: Thiele, Leander, et al.
Publicado: (2025)

M$^3$: Reframing Training Measures for Discretized Physical Simulations
por: Mei, Yuan, et al.
Publicado: (2026)

Mimicking Better by Matching the Approximate Action Distribution
por: Ramos, João A. Cândido, et al.
Publicado: (2023)

Temporal-Difference Variational Continual Learning
por: Melo, Luckeciano C., et al.
Publicado: (2024)

Stabilizing Temporal Difference Learning via Implicit Stochastic Recursion
por: Kim, Hwanwoo, et al.
Publicado: (2025)

Neural Dynamical Operator: Continuous Spatial-Temporal Model with Gradient-Based and Derivative-Free Optimization Methods
por: Chen, Chuanqi, et al.
Publicado: (2023)

Self-supervised Learning Method Using Transformer for Multi-dimensional Sensor Data Processing
por: Kai, Haruki, et al.
Publicado: (2025)

Transformers Can Learn Temporal Difference Methods for In-Context Reinforcement Learning
por: Wang, Jiuqi, et al.
Publicado: (2024)

Estimating counterfactual treatment outcomes over time in complex multiagent scenarios
por: Fujii, Keisuke, et al.
Publicado: (2022)

A Selective Learning Method for Temporal Graph Continual Learning
por: Liu, Hanmo, et al.
Publicado: (2025)

Time-Varying Graph Learning with Constraints on Graph Temporal Variation
por: Yokota, Haruki, et al.
Publicado: (2020)

Determinism in the Undetermined: Deterministic Output in Charge-Conserving Continuous-Time Neuromorphic Systems with Temporal Stochasticity
por: Yan, Jing, et al.
Publicado: (2026)

MobText-SISA: Efficient Machine Unlearning for Mobility Logs with Spatio-Temporal and Natural-Language Data
por: Yonekura, Haruki, et al.
Publicado: (2025)

An Analysis of Action-Value Temporal-Difference Methods That Learn State Values
por: Daley, Brett, et al.
Publicado: (2025)

Memory-Efficient FPGA Implementation of Stochastic Simulated Annealing
por: Shin, Duckgyu, et al.
Publicado: (2026)

Extending Differential Temporal Difference Methods for Episodic Problems
por: De Asis, Kris, et al.
Publicado: (2026)

Sequence Diffusion Model for Temporal Link Prediction in Continuous-Time Dynamic Graph
por: Duc, Nguyen Minh, et al.
Publicado: (2026)

Approximating Simple ReLU Networks based on Spectral Decomposition of Fisher Information
por: Ho, Ka Long Keith, et al.
Publicado: (2025)

Cochain Perspectives on Temporal-Difference Signals for Learning Beyond Markov Dynamics
por: Zhang, Zuyuan, et al.
Publicado: (2026)

Simplifying Deep Temporal Difference Learning
por: Gallici, Matteo, et al.
Publicado: (2024)

An Analysis of Quantile Temporal-Difference Learning
por: Rowland, Mark, et al.
Publicado: (2023)

On the Statistical Benefits of Temporal Difference Learning
por: Cheikhi, David, et al.
Publicado: (2023)

Temporal Difference Flows
por: Farebrother, Jesse, et al.
Publicado: (2025)

PINE: Pruning Boosted Tree Ensembles with Conformal In-Distribution Prediction Equivalence
por: Yajima, Haruki, et al.
Publicado: (2026)

Data-driven simulator of multi-animal behavior with unknown dynamics via offline and online reinforcement learning
por: Fujii, Keisuke, et al.
Publicado: (2025)

The compressible Neural Particle Method for Simulating Compressible Viscous Fluid Flows
por: Shibukawa, Masato, et al.
Publicado: (2025)

A Neural Model of Rule Discovery with Relatively Short-Term Sequence Memory
por: Arakawa, Naoya
Publicado: (2024)

Reinforcement Learning From State and Temporal Differences
por: Weaver, Lex, et al.
Publicado: (2025)

Towards Parameter-Free Temporal Difference Learning
por: Li, Yunxiang, et al.
Publicado: (2026)

Temporal Difference Learning with Constrained Initial Representations
por: Lyu, Jiafei, et al.
Publicado: (2026)

New Versions of Gradient Temporal Difference Learning
por: Lee, Donghwan, et al.
Publicado: (2021)

Recurrent Stochastic Configuration Networks for Temporal Data Analytics
por: Wang, Dianhui, et al.
Publicado: (2024)