LQL turns n-step action-sequence lower bounds into a practical hinge-loss stabilizer for off-policy Q-learning without extra networks or forward passes.
Revisiting the Minimalist Approach to Offline Reinforcement Learning, October 2023
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
baseline 1
citation-polarity summary
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Introduces relativised options and hierarchical abstraction to reuse experience across similar contexts in offline GCRL, with two algorithms demonstrating performance gains.
RankQ augments temporal-difference Q-learning with a multi-term self-supervised ranking loss to enforce structured action ordering, yielding competitive or better results than prior methods on D4RL and large gains in vision-based robot fine-tuning.
citing papers explorer
-
Long-Horizon Q-Learning: Accurate Value Learning via n-Step Inequalities
LQL turns n-step action-sequence lower bounds into a practical hinge-loss stabilizer for off-policy Q-learning without extra networks or forward passes.
-
Abstraction for Offline Goal-Conditioned Reinforcement Learning
Introduces relativised options and hierarchical abstraction to reuse experience across similar contexts in offline GCRL, with two algorithms demonstrating performance gains.
-
RankQ: Offline-to-Online Reinforcement Learning via Self-Supervised Action Ranking
RankQ augments temporal-difference Q-learning with a multi-term self-supervised ranking loss to enforce structured action ordering, yielding competitive or better results than prior methods on D4RL and large gains in vision-based robot fine-tuning.