Average-Reward Policy Identification from a Single Continuing Trajectory
cs.LG
Submitted: 2026-10-14
Updated: 2026-10-14
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Sources
- Sample Complexity of Average-Reward Q-Learning: From Single-agent to Federated Reinforcement Learning
- Non-Asymptotic Best Policy Identification Guarantees in Online Reinforcement Learning
- Learning Policy from a Single Trajectory in Average-Reward Markov Decision Process
- Near Sample-Optimal Reduction-based Policy Learning for Average Reward MDP
Related papers
- Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
- AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
- Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
- Convergence issues in Relational Concept Analysis based on AOC-posets
- Beliefs Beyond Posteriors: The Predictive Cost of ELBO Binding
- Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks