arXiv Machine Learning
Reinforcement Learning with Segment Reward Feedback under Linear Function Approximation
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SP3O: Reinforcement Learning from Segment Preferences without Reward Modeling
arXiv:2608. 02951v1 Announce Type: cross Abstract: Preference-based reinforcement learning (PbRL) for general stochastic MDPs often requires training a reward model.
arXiv Machine Learning
Jun 16
Fast Non-Episodic Finite-Horizon RL with K-Step Lookahead Thresholding
arXiv:2602. 00781v2 Announce Type: replace Abstract: Online reinforcement learning in non-episodic, finite-horizon MDPs remains underexplored and is challenged by the need to estimate returns to a fixed terminal time.
Hugging Face Trending Papers
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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...
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.
arXiv Machine Learning
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PathBridger: Subgoal Bridges for Offline Goal-Conditioned Reinforcement Learning
arXiv:2608.29061v1 Announce Type: new Abstract: Offline goal-conditioned reinforcement learning (GCRL) aims to learn policies for reaching diverse goals entirely from fixed trajectory data. Long-hori...
arXiv Machine Learning
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Limiting-Kernel Q($\lambda$): Bridging Short and Long Horizons
Limiting‑Kernel Q(λ) (LKQL) is an off‑policy value estimator that blends n‑step truncation with a long‑horizon approximation based on the limiting kernel. It maintains the computational efficiency of n‑step methods while improving policy evaluation accuracy, especially for long‑horizon tasks. The authors prove faster convergence of LKQL’s operator under aperiodicity and near‑on‑policy conditions, and demonstrate empirical gains on MuJoCo continuous‑control benchmarks.