arXiv Machine Learning

Primal-Dual Representation Learning for Low-Rank Constrained MDPs

The paper introduces REP-PD-TEG, a representation‑learning algorithm for low‑rank continuous‑state constrained Markov Decision Processes (CMDPs) that achieves optimal ≥O(√K) regret in the soft cumulative‑violation setting while maintaining a ≥O(K^{3/4}) sample bound when exploratory actions are penalised. For the stricter hard cumulative‑violation scenario, REP-PD-H-TEG is proposed, which uses a bonus‑corrected lower‑confidence utility certificate and a stabilized dual search to guarantee both regret and hard‑violation bounds. Together, these methods provide the first theoretically guaranteed representation‑learning framework for low‑rank continuous‑state CMDPs with both soft and hard violation controls.

arXiv Machine Learning

Oct 3

Rate-Optimal Algorithm for Adversarial Linear CMDPs

The paper introduces a new primal–dual algorithm for episodic adversarial linear constrained Markov decision processes (CMDPs) with unknown transitions. It achieves a rate‑optimal ×O(√K) regret and cumulative constraint violation, improving upon the previous ×O(K^{3/4}) bound and eliminating the need for Slater’s condition. The method combines adaptive FTRL, contracted value estimation, and an exponential Lyapunov function, enabling uniform concentration over the value function class and computational efficiency independent of the state‑space size.

By Kihyun Yu, Honghao Wei, Dabeen Lee

reinforcement-learning safety

arXiv Machine Learning

Jun 9

Reformulate LLM Reinforcement Learning for Efficient Training under Black-box Discrepancy

arXiv:2606. 08779v1 Announce Type: new Abstract: Reinforcement Learning (RL) has emerged as a pivotal post-training paradigm, yet it frequently suffers from unpredictable sub-optimum performance or even training collapses.

By Jiashun Liu, Runze Liu, Xu Wan, Jing Liang, Hongyao Tang, Ling Pan

llms reinforcement-learning

arXiv Machine Learning

Oct 7

Decision-Focused Learning in MDPs: An Occupancy Measure Approach

arXiv:2610.08384v1 Announce Type: new Abstract: In this work, we consider decision-focused learning (DFL) for a Markov decision process (MDP), where existing methods differentiate through the KKT con...

By Zihao Zhao, Ashwath K. Karunakaram, Ali Eshragh, Yuexing Li, Kai Wang

reinforcement-learning

Hugging Face Trending Papers

Oct 6

Decision-Focused Learning in MDPs: An Occupancy Measure Approach

In this work, we consider decision-focused learning (DFL) for a Markov decision process (MDP), where existing methods differentiate through the KKT conditions of the Bellman equation and require solvi...

reinforcement-learning

arXiv Machine Learning

Sep 21

Near-Optimal Primal-Dual Algorithm for Learning Linear Mixture CMDPs with Adversarial Rewards

arXiv:2603.27884v2 Announce Type: replace Abstract: We study safe reinforcement learning in finite-horizon linear mixture constrained Markov decision processes (CMDPs) with adversarial rewards under...

By Kihyun Yu, Seoungbin Bae, Dabeen Lee

reinforcement-learning safety

arXiv Machine Learning

Aug 4

Near-Optimal Reinforcement Learning for Constrained Recurrence Objectives

arXiv:2511. 19849v2 Announce Type: replace-cross Abstract: Recurrence objectives, where a target region must be visited infinitely often, are a fundamental class of specifications for Markov decision processes (MDPs) and form the core of $\omega$-regular and linear temporal logic (LTL) objectives.

By Dominik Wagner, Leon Witzman, Luke Ong

reinforcement-learning safety

arXiv Machine Learning

Oct 2

Learning Infinite-Horizon Average-Reward CMDPs via State Augmentation

The paper introduces a computationally efficient algorithm for infinite-horizon average-reward constrained Markov decision processes (CMDPs) under weak communication. It achieves a high-probability regret and cumulative constraint violation of ×O(√T) in the tabular setting, matching optimal dependence up to logarithmic factors. The method augments the state with cumulative constraint violation, reshapes rewards using a Huber potential, and applies finite-horizon approximation with optimistic value iteration to maintain bounded per-step rewards.

By Kihyun Yu, Seoungbin Bae, Dabeen Lee

reinforcement-learning

arXiv Machine Learning

Sep 24

Value Mirror Descent for Reinforcement Learning

arXiv:2604. 06039v2 Announce Type: replace-cross Abstract: Value iteration-type methods have been extensively studied for computing a nearly optimal value function in reinforcement learning (RL).

By Zhichao Jia, Guanghui Lan

reinforcement-learning

arXiv Machine Learning

Sep 29

Learning Chance-Constrained MDPs with Bellman Distributional Certificates

The paper introduces a new approach to learning chance-constrained Markov decision processes (CCMDPs) using a Bellman distributional certificate. It provides both model-based and model-free algorithms with theoretical guarantees, including matching upper and lower bounds for tabular discounted CCMDPs with bounded successor support. Numerical experiments on synthetic CCMDPs and an IEEE 14-bus energy storage benchmark demonstrate the safety and effectiveness of the proposed methods.

By Chenbei Lu, Hongyu Yi

reinforcement-learning benchmarks safety

Hugging Face Trending Papers

Sep 30

Learning Infinite-Horizon Average-Reward CMDPs via State Augmentation

We study infinite-horizon average-reward constrained Markov decision processes (CMDPs) under the weakly communicating assumption. Existing high-probability guarantees for this setting either require c...

reinforcement-learning

arXiv Machine Learning

Jul 20

Robust Peak-cost Constrained Reinforcement Learning

arXiv:2607. 15457v1 Announce Type: new Abstract: We study robust peak-cost constrained reinforcement learning (RP-CRL), where the objective is to maximize expected reward while controlling the maximum cost encountered along a trajectory.

By Shilpa Mukhopadhyay, Sourav Ganguly, Santosh Mohan Rajkumar, Honghao Wei, Debdipta Goswami, Arnob Ghosh

reinforcement-learning safety

arXiv Machine Learning

Sep 30

Optimal Multi-Reward Reinforcement Learning

arXiv:2609.36486v1 Announce Type: new Abstract: We study an unknown-transition finite-horizon Markov decision process (MDP) with a finite collection of known reward functions $\{r^1, r^2, \ldots, r^M...

By Zijun Chen, Zihan Zhang

reinforcement-learning

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