Cover Image

Reinforcement Learning with Multi-Step Lookahead Information Via Adaptive Batching

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Merlis, Nadav
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Reinforcement Learning with Lookahead Information
by: Merlis, Nadav
Published: (2024)

The Value of Reward Lookahead in Reinforcement Learning
by: Merlis, Nadav, et al.
Published: (2024)

On the Hardness of Reinforcement Learning with Transition Look-Ahead
by: Pla, Corentin, et al.
Published: (2025)

On Bits and Bandits: Quantifying the Regret-Information Trade-off
by: Shufaro, Itai, et al.
Published: (2024)

Adaptive Bandit Algorithms for Contextual Matching Markets
by: Lin, Shiyun, et al.
Published: (2026)

Online Linear Regression with Paid Stochastic Features
by: Merlis, Nadav, et al.
Published: (2025)

EARL-BO: Reinforcement Learning for Multi-Step Lookahead, High-Dimensional Bayesian Optimization
by: Cheon, Mujin, et al.
Published: (2024)

Stable Matching with Ties: Approximation Ratios and Learning
by: Lin, Shiyun, et al.
Published: (2024)

AdaBatchGrad: Combining Adaptive Batch Size and Adaptive Step Size
by: Ostroukhov, Petr, et al.
Published: (2024)

Improved Algorithms for Contextual Dynamic Pricing
by: Tullii, Matilde, et al.
Published: (2024)

Sample-Efficiency in Multi-Batch Reinforcement Learning: The Need for Dimension-Dependent Adaptivity
by: Johnson, Emmeran, et al.
Published: (2023)

Fast Non-Episodic Finite-Horizon RL with K-Step Lookahead Thresholding
by: Xu, Jiamin, et al.
Published: (2026)

Scalable On-Policy Reinforcement Learning via Adaptive Batch Scaling
by: Park, Jongchan
Published: (2026)

The Equilibrium Response of Atmospheric Machine-Learning Models to Uniform Sea Surface Temperature Warming
by: Zhang, Bosong, et al.
Published: (2025)

Imagine-then-Plan: Agent Learning from Adaptive Lookahead with World Models
by: Liu, Youwei, et al.
Published: (2026)

Distilling Reinforcement Learning into Single-Batch Datasets
by: Wilhelm, Connor, et al.
Published: (2025)

Next-Depth Lookahead Tree
by: Lee, Jaeho, et al.
Published: (2025)

Generalization and Optimization of SGD with Lookahead
by: Li, Kangcheng, et al.
Published: (2025)

Lookahead Counterfactual Fairness
by: Zuo, Zhiqun, et al.
Published: (2024)

Adaptive Deadline and Batch Layered Synchronized Federated Learning
by: Goren, Asaf, et al.
Published: (2025)

Learning with Foresight: Enhancing Neural Routing Policy via Multi-Node Lookahead Prediction
by: Jiang, Xia, et al.
Published: (2026)

Labeled TrustSet Guided: Batch Active Learning with Reinforcement Learning
by: Cui, Guofeng, et al.
Published: (2026)

Causal Attention with Lookahead Keys
by: Song, Zhuoqing, et al.
Published: (2025)

Policy Mirror Descent with Lookahead
by: Protopapas, Kimon, et al.
Published: (2024)

Multi-Label Adaptive Batch Selection by Highlighting Hard and Imbalanced Samples
by: Zhou, Ao, et al.
Published: (2024)

Switching the Loss Reduces the Cost in Batch (Offline) Reinforcement Learning
by: Ayoub, Alex, et al.
Published: (2024)

Lookahead Path Likelihood Optimization for Diffusion LLMs
by: Liu, Xuejie, et al.
Published: (2026)

ADAM Optimization with Adaptive Batch Selection
by: Kim, Gyu Yeol, et al.
Published: (2025)

Smaller Batches, Bigger Gains? Investigating the Impact of Batch Sizes on Reinforcement Learning Based Real-World Production Scheduling
by: Müller, Arthur, et al.
Published: (2024)

Scaling Speculative Decoding with Lookahead Reasoning
by: Fu, Yichao, et al.
Published: (2025)

Lookahead identification in adversarial bandits: accuracy and memory bounds
by: Brukhim, Nataly, et al.
Published: (2026)

Learning To Sample From Diffusion Models Via Inverse Reinforcement Learning
by: Bourdrez, Constant, et al.
Published: (2026)

Inference for Batched Adaptive Experiments
by: Kemper, Jan, et al.
Published: (2025)

Lookahead Drifting Model
by: Zhang, Guoqiang, et al.
Published: (2026)

EMA-Nesterov: Stabilizing Nesterov's Lookahead for Accelerated Deep Learning Optimization
by: Yau, Chung-Yiu, et al.
Published: (2026)

Towards Batch-to-Streaming Deep Reinforcement Learning for Continuous Control
by: De Monte, Riccardo, et al.
Published: (2026)

Offline Reinforcement Learning for LLM Multi-Step Reasoning
by: Wang, Huaijie, et al.
Published: (2024)

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization
by: Palenicek, Daniel, et al.
Published: (2025)

Thinking into the Future: Latent Lookahead Training for Transformers
by: Noci, Lorenzo, et al.
Published: (2026)

Reinforcement Learning with Elastic Time Steps
by: Wang, Dong, et al.
Published: (2024)