arXiv AI July 9, 2026 By Ioanna-Yvonni Tsaknaki, Andrea Macr\`i, Fabrizio Lillo

Can Reinforcement Learning Efficiently Discover Price Manipulation?

arXiv:2607. 06121v1 Announce Type: cross Abstract: In this paper, we investigate whether a model-free RL agent can identify and exploit price manipulation opportunities more effectively than a traditional model-based approach that assumes correct specification of the data-generating process but relies on noisy parameter estimates.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv Machine Learning

Jul 7

Trading Confidence: Comprehensive Uncertainty Estimation in Algorithmic Trading

arXiv:2607. 02864v1 Announce Type: new Abstract: Reinforcement Learning (RL) has emerged as a powerful approach in financial trading, enabling agents to learn optimal strategies through direct market interaction.

By Lin Li, Li Rong Wang, Hsuan Fu, Xiuyi Fan

agents reinforcement-learning

arXiv Machine Learning

Jun 4

Dynamic Multi-Pair Trading Strategy in Cryptocurrency Markets with Deep Reinforcement Learning

arXiv:2606. 04574v1 Announce Type: new Abstract: This study aims to determine whether the application of Deep Reinforcement Learning (DRL) as a specialized execution overlay can enhance pair trading in highly volatile cryptocurrency markets.

By Damian Lebied\'z, Robert \'Slepaczuk

agents reinforcement-learning

arXiv Machine Learning

Jun 30

Theoretical Foundations and Effective Algorithms for Policy-Aware Simulator Learning

arXiv:2605. 29032v2 Announce Type: replace Abstract: Model-based reinforcement learning (MBRL) agents typically learn world models by minimizing predictive loss.

By Christoph Dann, Yishay Mansour, Mehryar Mohri

agents reinforcement-learning safety

arXiv AI

Jun 18

Forecasting what Matters: Decision-Focused RL for Controlled EV Charging with Unknown Departure Times

arXiv:2606. 19199v1 Announce Type: cross Abstract: The recent growth of EV adoption poses challenges for power systems, including increased peak demand and potential grid instability.

By Giuseppe Gabriele, Fabio Pavirani, Seyed Soroush Karimi Madahi, Chris Develder

agents reinforcement-learning

arXiv Machine Learning

Jun 25

Auto-exploration for online reinforcement learning

arXiv:2512. 06244v2 Announce Type: replace Abstract: The exploration-exploitation dilemma in reinforcement learning (RL) is a fundamental challenge to efficient RL algorithms.

By Caleb Ju, Guanghui Lan

reinforcement-learning

arXiv AI

Jun 8

Just-In-Time Reinforcement Learning: Continual Learning in LLM Agents Without Gradient Updates

arXiv:2601. 18510v2 Announce Type: replace-cross Abstract: While Large Language Model (LLM) agents excel at general tasks, they inherently struggle with continual adaptation due to the frozen weights after deployment.

By Yibo Li, Zijie Lin, Ailin Deng, Xuan Zhang, Yufei He, Shuo Ji, Tri Cao, Bryan Hooi

llms agents reinforcement-learning fine-tuning benchmarks

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Trading Confidence: Comprehensive Uncertainty Estimation in Algorithmic Trading

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Theoretical Foundations and Effective Algorithms for Policy-Aware Simulator Learning

Forecasting what Matters: Decision-Focused RL for Controlled EV Charging with Unknown Departure Times

Auto-exploration for online reinforcement learning

Just-In-Time Reinforcement Learning: Continual Learning in LLM Agents Without Gradient Updates