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.

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

reinforcement-learning

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

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agents reinforcement-learning

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llms agents reinforcement-learning

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llms benchmarks

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agents fine-tuning

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reinforcement-learning safety

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Adapt Only When It Pays: Budgeted Decision-Loss Priority for Delayed Online Time-Series Adaptation

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

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