arXiv RL Theory @RLtheoryPapers
Daily updates of reinforcement learning theory papers on arXiv Joined June 2025-
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Reinforcement Learning with Action-Triggered Observations arxiv.org/abs/2510.02149
Finite-Time Bounds for Distributionally Robust TD Learning with Linear Function Approximation arxiv.org/abs/2510.01721
On the (almost) Global Exponential Convergence of the Overparameterized Policy Optimization for the LQR Problem arxiv.org/abs/2510.02140
Rethinking KL Regularization in RLHF: From Value Estimation to Gradient Optimization arxiv.org/abs/2510.01555
Lipschitz Bandits with Stochastic Delayed Feedback arxiv.org/abs/2510.00309
Initial Distribution Sensitivity of Constrained Markov Decision Processes arxiv.org/abs/2510.00348
Extensions of Robbins-Siegmund Theorem with Applications in Reinforcement Learning arxiv.org/abs/2509.26442
Online Decision Making with Generative Action Sets arxiv.org/abs/2509.25777
Functional Critic Modeling for Provably Convergent Off-Policy Actor-Critic arxiv.org/abs/2509.22964
Sampling Complexity of TD and PPO in RKHS arxiv.org/abs/2509.24991
Bridging Discrete and Continuous RL: Stable Deterministic Policy Gradient with Martingale Characterization arxiv.org/abs/2509.23711
Markov Decision Processing Networks arxiv.org/abs/2509.24541
Optimism as Risk-Seeking in Multi-Agent Reinforcement Learning arxiv.org/abs/2509.24047
Asynchronous Policy Gradient Aggregation for Efficient Distributed Reinforcement Learning arxiv.org/abs/2509.24305
Stability Analysis of An Integrated Multistage Stochastic Programming and Markov Decision Process Problem arxiv.org/abs/2509.22194
A regret minimization approach to fixed-point iterations arxiv.org/abs/2509.21653
EPO: Entropy-regularized Policy Optimization for LLM Agents Reinforcement Learning arxiv.org/abs/2509.22576
Structural Information-based Hierarchical Diffusion for Offline Reinforcement Learning arxiv.org/abs/2509.21942
Towards Efficient Online Exploration for Reinforcement Learning with Human Feedback arxiv.org/abs/2509.22633
Benefits and Pitfalls of Reinforcement Learning for Language Model Planning: A Theoretical Perspective arxiv.org/abs/2509.22613
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