A distributed zeroth-order policy gradient algorithm allows networked agents to collaboratively optimize policies using only local human preference feedback on H-horizon trajectory pairs from kappa-hop neighborhoods, with proven convergence to an epsilon-stationary point.
Exploration-driven policy optimization in rlhf: Theoretical insights on efficient data utilization
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
verdicts
UNVERDICTED 4representative citing papers
Primal-dual policy gradient algorithms achieve global non-asymptotic convergence for safe RLHF cast as infinite-horizon discounted CMDPs without fitting reward models.
A novel robust asynchronous Q-learning algorithm achieves finite-time convergence rates that match clean-data bounds up to an additive term proportional to the corruption fraction, with a matching information-theoretic lower bound.
OHIRL separates next-packet prediction, residual dynamics, a fixed recovery-positive evaluator, and policy learning to achieve high sign and action accuracy in reward-free perceptual tasks where standard reward proxies fail.
citing papers explorer
-
Distributed Zeroth-Order Policy Gradient for Networked Multi-agent Reinforcement Learning from Human Feedback
A distributed zeroth-order policy gradient algorithm allows networked agents to collaboratively optimize policies using only local human preference feedback on H-horizon trajectory pairs from kappa-hop neighborhoods, with proven convergence to an epsilon-stationary point.
-
Policy Gradient Primal-Dual Method for Safe Reinforcement Learning from Human Feedback
Primal-dual policy gradient algorithms achieve global non-asymptotic convergence for safe RLHF cast as infinite-horizon discounted CMDPs without fitting reward models.
-
Corruption-Tolerant Asynchronous Q-Learning with Near-Optimal Rates
A novel robust asynchronous Q-learning algorithm achieves finite-time convergence rates that match clean-data bounds up to an additive term proportional to the corruption fraction, with a matching information-theoretic lower bound.
-
Online Reward-Punishment Learning from Fixed-Channel Perceptual Event Streams without Environment Rewards
OHIRL separates next-packet prediction, residual dynamics, a fixed recovery-positive evaluator, and policy learning to achieve high sign and action accuracy in reward-free perceptual tasks where standard reward proxies fail.