arXiv Machine Learning August 6, 2026 By Yifan Wu, Junjie Lei, Wenjie Huang

Adaptive Finite-Budget Training for CVaR Risk-Aware Q-Learning

Read the original on arXiv Machine Learning →

arXiv:2608. 04305v1 Announce Type: new Abstract: Risk-aware Q-learning (RaQL) provides a model-free, two-timescale estimator for dynamic risk objectives, but its finite-budget behavior remains fragile: fixed inner-loop hyperparameters can produce unstable value estimates, persistent Bellman residuals, and inefficient sample reuse.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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

Jul 20

CLaC@FinMMEval 2026 Task 3: Sentiment-Augmented Deep Reinforcement Learning for Active Trading -- An Alpha-Reward Approach

arXiv:2607. 16028v1 Announce Type: new Abstract: This paper presents our system for Task 3 of the CLEF 2026 FinMMEval Lab, which requires daily long, flat, or short trading decisions for Bitcoin (BTC) and Tesla (TSLA) using news and historical market data.

By Andrei Neagu, Eeham Khan, Leila Kosseim

llms agents reinforcement-learning

arXiv AI

2d ago

PPO-HRAP: Proximal Policy Optimization with a Hybrid Regime-Aware Policy for Risk-Controlled Trading

PPO‑HRAP introduces a hybrid regime‑aware policy that blends Proximal Policy Optimization with a volatility‑conditioned regime prior to balance upside participation and drawdown control in trading. The agent uses market and portfolio features, rewards that combine log return, VIX‑conditioned drawdown penalty, exposure deviation, and turnover cost, and outputs a blended action between the PPO actor and the regime‑derived target exposure. In backtests on SPY (2020‑2022) it achieved a 27.62% total return, 8.48% annualized return, and reduced maximum drawdown from 34.10% to 18.47%, while maintaining stable performance across multiple seeds and ranking first on total return and Sharpe ratio in single‑run cross‑asset tests on QQQ and DIA.

By Duong Hien Chi Kien, Thanh Trung Huynh

agents reinforcement-learning

arXiv AI

Aug 12

Beyond Forecasting: Recasting Volatility Control as a Routing Problem

arXiv:2608. 10375v1 Announce Type: cross Abstract: Volatility control converts risk estimates into portfolio exposure, yet existing approaches often rely on a fixed volatility estimator or a pre-defined control rule that may not adapt to changing market conditions.

By Hongji Pu, Leyang Zhou

llms benchmarks

arXiv AI

Jun 2

Regime-Adaptive Continual Learning for Portfolio Management

arXiv:2606. 00143v1 Announce Type: cross Abstract: Financial markets are inherently non-stationary, exhibiting frequent regime shifts and structural changes that render traditional Portfolio Management (PM) approaches ineffective.

By Chaofan Pan, Lingfei Ren, Linbo Xiong, Yonghao Li, Wei Wei, Xin Yang

agents fine-tuning

arXiv AI

Jun 10

Mitigating Bias in Low-SNR Financial Reinforcement Learning via Quantum Representations

arXiv:2606. 10448v1 Announce Type: cross Abstract: The financial market is a typical low signal-to-noise ratio (SNR) setting, which often destabilizes off-policy maximum-entropy methods like Soft Actor-Critic (SAC).

By Zeyu Liu, Xuanzhi Feng, Sing Kwong Lai, Yuanchen Gao, Xiaoyi Pang, Hualei Zhang, Jingcai Guo, Jie Zhang, Song Guo

reinforcement-learning safety

One email a morning, machine-written

Related stories

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

CLaC@FinMMEval 2026 Task 3: Sentiment-Augmented Deep Reinforcement Learning for Active Trading -- An Alpha-Reward Approach

PPO-HRAP: Proximal Policy Optimization with a Hybrid Regime-Aware Policy for Risk-Controlled Trading

Beyond Forecasting: Recasting Volatility Control as a Routing Problem

Regime-Adaptive Continual Learning for Portfolio Management

Mitigating Bias in Low-SNR Financial Reinforcement Learning via Quantum Representations