9 citations · 9 across the 2 of their papers we have counts for
2 papers
cs.LG2026
PAWS: Preference Learning with Advantage-Weighted Segments
Aleksandar Taranovic, Onur Celik, Niklas Freymuth +6
Preference-based reinforcement learning (PbRL) learns policies from human trajectory-level comparisons, avoiding explicit reward design and expert demonstrations. Existing methods…
cs.LG2017★ 9 cited
Learning Inverse Statics Models Efficiently
Rania Rayyes, Daniel Kubus, Carsten Hartmann +1
Online Goal Babbling and Direction Sampling are recently proposed methods for direct learning of inverse kinematics mappings from scratch even in high-dimensional sensorimotor spac…