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arXiv Machine Learning

On the Convergence of Success Conditioning for Policy Optimization

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arXiv Machine Learning
Oct 3

Towards Optimal Policy Improvement

The paper introduces a framework for optimal policy improvement in reinforcement learning, defining it as the best single update under given constraints. It shows that restricting improvement to a subset of states is equivalent to solving an induced Markov Decision Process, linking planning with explicit or implicit models to optimal policy improvement. The authors develop a novel operator for greedification under approximate evaluation, demonstrating empirical gains across several RL algorithms and settings.

By Yaniv Oren, Viliam Vadocz, Wiktor Zabka, Thomas Evers, Jan Robine, Wendelin B\"ohmer, Matthijs T. J. Spaan, Martha White, Hendrik Baier, Fenghui Yu
reinforcement-learning
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arXiv Machine Learning
Oct 9

Closing the Horizon Gap in Policy Optimization for Adversarial MDPs

The paper addresses policy optimization in online episodic tabular MDPs with adversarial losses and bandit feedback. By employing regularized Q‑functions, the authors eliminate the extra horizon factor present in previous policy‑optimization regret bounds, achieving high‑probability regret of ≥O(√{HS(H+A)T}) for known transitions and ≥O(HS√{AT}) for unknown transitions. These results improve horizon dependence and match the best-known bounds, with extensions to adversarial linear‑mixture MDPs.

By Mingyi Li, Taira Tsuchiya
reinforcement-learningsafety
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arXiv Machine Learning
Jul 1

End-to-End Efficient RL for Linear Bellman Complete MDPs with Deterministic Transitions

arXiv:2603. 23461v2 Announce Type: replace Abstract: We study reinforcement learning (RL) with linear function approximation in Markov Decision Processes (MDPs) satisfying \emph{linear Bellman completeness} -- a fundamental setting where the Bellman backup of any linear value function remains linear.

By Zakaria Mhammedi, Alexander Rakhlin, Nneka Okolo
reinforcement-learning
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arXiv Machine Learning
Oct 2

Fast Regularized Policy Mirror Descent with One-Step TD Updates

The paper introduces Fast Regularized Policy Mirror Descent (PMD) that pairs policy updates with a single temporal-difference (TD) critic step. It proves global linear convergence for finite discounted MDPs using exact coordinate-wise Bellman updates and any positive actor stepsize, regardless of critic initialization. For stochastic TD-PMD with strongly convex mirror maps, the authors achieve an expected value gap of ε after “~O(1/((1-γ)^5 σ_b ε))” transitions, without requiring trajectory resets, generative models, or nested policy evaluation loops.

By Qipei Chen, Wenye Li, Yule Sun, Ke Wei
reinforcement-learning
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