D4RL supplies new offline RL benchmarks and datasets from expert and mixed sources to expose weaknesses in existing algorithms and standardize evaluation.
Off-policy deep reinforcement learning without exploration
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
roles
background 1polarities
background 1representative citing papers
RankQ adds a self-supervised ranking loss to Q-learning to learn structured action orderings, yielding competitive or better performance than prior methods on D4RL benchmarks and large gains in vision-based robot fine-tuning.
DAWM introduces a modular diffusion world model with an inverse dynamics model to produce complete synthetic transitions that improve conservative offline RL algorithms like TD3BC and IQL on D4RL tasks.
Behavior-regularized actor-critic methods achieve strong offline RL results with simple regularization, rendering many recent technical additions unnecessary.
A preference fine-tuning method for LLMs that combines context augmentation, theory-driven preference pair construction, curriculum learning, and a density estimation support constraint to produce domain-aligned review responses with reduced hallucinations and over-conservatism.
Offline RL promises to extract high-utility policies from static datasets but faces fundamental challenges that current methods only partially address.
citing papers explorer
-
D4RL: Datasets for Deep Data-Driven Reinforcement Learning
D4RL supplies new offline RL benchmarks and datasets from expert and mixed sources to expose weaknesses in existing algorithms and standardize evaluation.
-
RankQ: Offline-to-Online Reinforcement Learning via Self-Supervised Action Ranking
RankQ adds a self-supervised ranking loss to Q-learning to learn structured action orderings, yielding competitive or better performance than prior methods on D4RL benchmarks and large gains in vision-based robot fine-tuning.
-
DAWM: Diffusion Action World Models for Offline Reinforcement Learning via Action-Inferred Transitions
DAWM introduces a modular diffusion world model with an inverse dynamics model to produce complete synthetic transitions that improve conservative offline RL algorithms like TD3BC and IQL on D4RL tasks.
-
Behavior Regularized Offline Reinforcement Learning
Behavior-regularized actor-critic methods achieve strong offline RL results with simple regularization, rendering many recent technical additions unnecessary.
-
Align Generative Artificial Intelligence with Human Preferences: A Novel Large Language Model Fine-Tuning Method for Online Review Management
A preference fine-tuning method for LLMs that combines context augmentation, theory-driven preference pair construction, curriculum learning, and a density estimation support constraint to produce domain-aligned review responses with reduced hallucinations and over-conservatism.
-
Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems
Offline RL promises to extract high-utility policies from static datasets but faces fundamental challenges that current methods only partially address.