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Showing 151–200 of 226 papers

TitleStatusHype
Diffusion Model Predictive Control—0
Diffusion Policies for Out-of-Distribution Generalization in Offline Reinforcement Learning—0
Diffusion World Model: Future Modeling Beyond Step-by-Step Rollout for Offline Reinforcement Learning—0
Augmenting Offline Reinforcement Learning with State-only Interactions—0
Offline Diversity Maximization Under Imitation Constraints—0
Diverse Transformer Decoding for Offline Reinforcement Learning Using Financial Algorithmic Approaches—0
DOMAIN: MilDly COnservative Model-BAsed OfflINe Reinforcement Learning—0
DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization—0
DRDT3: Diffusion-Refined Decision Test-Time Training Model—0
Elastic Decision Transformer—0
EMaQ: Expected-Max Q-Learning Operator for Simple Yet Effective Offline and Online RL—0
Emergent Agentic Transformer from Chain of Hindsight Experience—0
Enhancing Decision Transformer with Diffusion-Based Trajectory Branch Generation—0
Finer Behavioral Foundation Models via Auto-Regressive Features and Advantage Weighting—0
Fine-Tuning Offline Reinforcement Learning with Model-Based Policy Optimization—0
Flow to Control: Offline Reinforcement Learning with Lossless Primitive Discovery—0
Forward KL Regularized Preference Optimization for Aligning Diffusion Policies—0
Fourier Controller Networks for Real-Time Decision-Making in Embodied Learning—0
From Novelty to Imitation: Self-Distilled Rewards for Offline Reinforcement Learning—0
Goal-Conditioned Data Augmentation for Offline Reinforcement Learning—0
Guided Data Augmentation for Offline Reinforcement Learning and Imitation Learning—0
Hierarchical Decision Transformer—0
HIPODE: Enhancing Offline Reinforcement Learning with High-Quality Synthetic Data from a Policy-Decoupled Approach—0
Imagination-Limited Q-Learning for Offline Reinforcement Learning—0
Improving Offline Reinforcement Learning with Inaccurate Simulators—0
Improving Offline RL by Blending Heuristics—0
Iteratively Refined Behavior Regularization for Offline Reinforcement Learning—0
IQL-TD-MPC: Implicit Q-Learning for Hierarchical Model Predictive Control—0
KAN v.s. MLP for Offline Reinforcement Learning—0
Know Your Boundaries: The Necessity of Explicit Behavioral Cloning in Offline RL—0
Koopman Q-learning: Offline Reinforcement Learning via Symmetries of Dynamics—0
Learning Computational Efficient Bots with Costly Features—0
Learning from Random Demonstrations: Offline Reinforcement Learning with Importance-Sampled Diffusion Models—0
Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network—0
Model-based Offline Reinforcement Learning with Local Misspecification—0
Model-Based Offline Reinforcement Learning with Adversarial Data Augmentation—0
Model-based trajectory stitching for improved behavioural cloning and its applications—0
MOORe: Model-based Offline-to-Online Reinforcement Learning—0
MOORL: A Framework for Integrating Offline-Online Reinforcement Learning—0
Multi-Objective Decision Transformers for Offline Reinforcement Learning—0
Offline Model-Based Reinforcement Learning with Anti-Exploration—0
Offline Reinforcement Learning with Imbalanced Datasets—0
Offline Reinforcement Learning with Adaptive Behavior Regularization—0
Offline Reinforcement Learning with Closed-Form Policy Improvement Operators—0
Offline Reinforcement Learning with Imputed Rewards—0
Offline Reinforcement Learning with On-Policy Q-Function Regularization—0
Offline Reinforcement Learning with Resource Constrained Online Deployment—0
Offline Trajectory Generalization for Offline Reinforcement Learning—0
On the Role of Discount Factor in Offline Reinforcement Learning—0
Binary Reward Labeling: Bridging Offline Preference and Reward-Based Reinforcement Learning—0
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