Cover Image

Towards Global Optimality for Practical Average Reward Reinforcement Learning without Mixing Time Oracles

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Patel, Bhrij, Suttle, Wesley A., Koppel, Alec, Aggarwal, Vaneet, Sadler, Brian M., Bedi, Amrit Singh, Manocha, Dinesh
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Confidence-Controlled Exploration: Efficient Sparse-Reward Policy Learning for Robot Navigation
by: Patel, Bhrij, et al.
Published: (2023)

Personalized Embodied Navigation for Portable Object Finding
by: Dorbala, Vishnu Sashank, et al.
Published: (2024)

Multi-LLM QA with Embodied Exploration
by: Patel, Bhrij, et al.
Published: (2024)

Code Comprehension then Auditing for Unsupervised LLM Evaluation
by: Patel, Bhrij, et al.
Published: (2024)

PIPER: Primitive-Informed Preference-based Hierarchical Reinforcement Learning via Hindsight Relabeling
by: Singh, Utsav, et al.
Published: (2024)

Achieving Zero Constraint Violation for Constrained Reinforcement Learning via Conservative Natural Policy Gradient Primal-Dual Algorithm
by: Bai, Qinbo, et al.
Published: (2022)

PARL: A Unified Framework for Policy Alignment in Reinforcement Learning from Human Feedback
by: Chakraborty, Souradip, et al.
Published: (2023)

DIPPER: Direct Preference Optimization to Accelerate Primitive-Enabled Hierarchical Reinforcement Learning
by: Singh, Utsav, et al.
Published: (2024)

On The Global Convergence Of Online RLHF With Neural Parametrization
by: Gaur, Mudit, et al.
Published: (2024)

Closing the Gap: Achieving Global Convergence (Last Iterate) of Actor-Critic under Markovian Sampling with Neural Network Parametrization
by: Gaur, Mudit, et al.
Published: (2024)

Beyond Joint Demonstrations: Personalized Expert Guidance for Efficient Multi-Agent Reinforcement Learning
by: Yu, Peihong, et al.
Published: (2024)

REBEL: Reward Regularization-Based Approach for Robotic Reinforcement Learning from Human Feedback
by: Chakraborty, Souradip, et al.
Published: (2023)

On The Sample Complexity Bounds In Bilevel Reinforcement Learning
by: Gaur, Mudit, et al.
Published: (2025)

MaxMin-RLHF: Alignment with Diverse Human Preferences
by: Chakraborty, Souradip, et al.
Published: (2024)

DMCA: Dense Multi-agent Navigation using Attention and Communication
by: Arul, Senthil Hariharan, et al.
Published: (2022)

A Sharper Global Convergence Analysis for Average Reward Reinforcement Learning via an Actor-Critic Approach
by: Ganesh, Swetha, et al.
Published: (2024)

LANCAR: Leveraging Language for Context-Aware Robot Locomotion in Unstructured Environments
by: Shek, Chak Lam, et al.
Published: (2023)

Deceptive Path Planning via Reinforcement Learning with Graph Neural Networks
by: Fatemi, Michael Y., et al.
Published: (2024)

Efficient $Q$-Learning and Actor-Critic Methods for Robust Average Reward Reinforcement Learning
by: Xu, Yang, et al.
Published: (2025)

Regret Analysis of Unichain Average Reward Constrained MDPs with General Parameterization
by: Satheesh, Anirudh, et al.
Published: (2026)

Regret Analysis of Average-Reward Unichain MDPs via an Actor-Critic Approach
by: Ganesh, Swetha, et al.
Published: (2025)

Finite-Sample Analysis of Policy Evaluation for Robust Average Reward Reinforcement Learning
by: Xu, Yang, et al.
Published: (2025)

Improved Sample Complexity For Diffusion Model Training Without Empirical Risk Minimizer Access
by: Gaur, Mudit, et al.
Published: (2025)

Constrained Reinforcement Learning with Average Reward Objective: Model-Based and Model-Free Algorithms
by: Aggarwal, Vaneet, et al.
Published: (2024)

Order-Optimal Regret with Novel Policy Gradient Approaches in Infinite-Horizon Average Reward MDPs
by: Ganesh, Swetha, et al.
Published: (2024)

On the Vulnerability of LLM/VLM-Controlled Robotics
by: Wu, Xiyang, et al.
Published: (2024)

BalancedDPO: Adaptive Multi-Metric Alignment
by: Tamboli, Dipesh, et al.
Published: (2025)

Quantum Speedups in Regret Analysis of Infinite Horizon Average-Reward Markov Decision Processes
by: Ganguly, Bhargav, et al.
Published: (2023)

On the Global Optimality of Policy Gradient Methods in General Utility Reinforcement Learning
by: Barakat, Anas, et al.
Published: (2024)

Global Convergence of Average Reward Constrained MDPs with Neural Critic and General Policy Parameterization
by: Satheesh, Anirudh, et al.
Published: (2026)

Signal attenuation enables scalable decentralized multi-agent reinforcement learning over networks
by: Suttle, Wesley A, et al.
Published: (2025)

Direct Preference Optimization for Primitive-Enabled Hierarchical RL: A Bilevel Approach
by: Singh, Utsav, et al.
Published: (2024)

Global Convergence for Average Reward Constrained MDPs with Primal-Dual Actor Critic Algorithm
by: Xu, Yang, et al.
Published: (2025)

Align-Pro: A Principled Approach to Prompt Optimization for LLM Alignment
by: Trivedi, Prashant, et al.
Published: (2025)

Generative Modeling with Continuous Flows: Sample Complexity of Flow Matching
by: Gaur, Mudit, et al.
Published: (2025)

Safety Recovery in Reasoning Models Is Only a Few Early Steering Steps Away
by: Ghosal, Soumya Suvra, et al.
Published: (2026)

SABER: A Stealthy Agentic Black-Box Attack Framework for Vision-Language-Action Models
by: Wu, Xiyang, et al.
Published: (2026)

Regret Analysis of Policy Gradient Algorithm for Infinite Horizon Average Reward Markov Decision Processes
by: Bai, Qinbo, et al.
Published: (2023)

Oracle-Robust Online Alignment for Large Language Models
by: Li, Zimeng, et al.
Published: (2026)

Q-GADMM: Quantized Group ADMM for Communication Efficient Decentralized Machine Learning
by: Elgabli, Anis, et al.
Published: (2019)