Hugging Face Trending Papers
V-SHARE: Provable Convergence Guarantees for Advantage-based Algorithms
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arXiv AI
Oct 16
V-SHARE: Provable Convergence Guarantees for Advantage-based Algorithms
V‑SHARE is an advantage‑based value‑learning algorithm that blends cross‑action value sharing with an importance‑weighted Bellman‑residual correction. The paper presents the first full theoretical analysis of such an algorithm, offering both asymptotic convergence and finite‑sample guarantees. It also introduces schedules that balance deterministic contraction against importance‑weighting noise, and a two‑timescale analysis that eliminates a residual‑weight threshold while maintaining convergence.
arXiv AI
Jun 30
Accelerating Q-learning through Efficient Value-Sharing across Actions
arXiv:2606. 29806v1 Announce Type: cross Abstract: Action-values are foundational to many control algorithms such as Q-learning.
arXiv AI
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Spectral Analysis of Dueling Q-Learning
arXiv:2607. 08340v1 Announce Type: cross Abstract: Q-learning is a fundamental algorithm in reinforcement learning (RL) for solving discounted Markov decision processes (MDPs) when the transition kernel is unknown.
arXiv AI
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Gated Q-learning: Add Off-Policy Bias to Taste
arXiv:2607. 28916v1 Announce Type: cross Abstract: Multistep credit assignment is critical for sample-efficient reinforcement learning, yet managing off-policy bias in Q-learning remains a fundamental challenge.
arXiv Machine Learning
Sep 10
Decision-Centered Abstractions via Orthogonal Estimation of Difference-of-Q Functions
The paper introduces state abstractions that preserve the difference of Q‑functions for offline reinforcement learning, aiming to exclude irrelevant dynamics from rich state data. It proposes a dynamic generalization of the R‑learner that uses orthogonal estimation and sparse learning to estimate the Q‑function contrast, achieving faster convergence and consistency under a margin condition. Experiments on simulated and simulator‑augmented real data show variance reductions and demonstrate that the necessary information for sequential decision‑making can be smaller than that required for full state prediction.
arXiv AI
Jun 16
Deep Q-Learning on H\"older Spaces
arXiv:2606. 16846v1 Announce Type: cross Abstract: We study the operator-theoretic core of Q-learning in continuous-time stochastic control with continuous states and actions.