SENIOR improves feedback efficiency and policy learning speed in PbRL by combining motion-distinction query selection via kernel density estimation with preference-guided intrinsic rewards, showing gains on simulated and real robot tasks.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
Deep reinforcement learning applied to measurement-based quantum feedback control achieves faster stabilization of random initial states to target entangled states in two- and three-qubit systems than Lyapunov feedback or alternative DRL reward designs.
citing papers explorer
-
SENIOR: Efficient Query Selection and Preference-Guided Exploration in Preference-based Reinforcement Learning
SENIOR improves feedback efficiency and policy learning speed in PbRL by combining motion-distinction query selection via kernel density estimation with preference-guided intrinsic rewards, showing gains on simulated and real robot tasks.
-
Fast State Stabilization using Deep Reinforcement Learning for Measurement-based Quantum Feedback Control
Deep reinforcement learning applied to measurement-based quantum feedback control achieves faster stabilization of random initial states to target entangled states in two- and three-qubit systems than Lyapunov feedback or alternative DRL reward designs.