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
5 Pith papers cite this work. Polarity classification is still indexing.
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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.
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
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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.
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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.
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Behavior Regularized Offline Reinforcement Learning
Behavior-regularized actor-critic methods achieve strong offline RL results with simple regularization, rendering many recent technical additions unnecessary.
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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.
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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.