awesome-offline-to-online-RL-papers
A list of Offline to Online RL papers (continually updated)
https://github.com/linhlpv/awesome-offline-to-online-RL-papers
Last synced: 19 days ago
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Papers
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Offline to Online
- MOORe: Model-based Offline-to-Online Reinforcement Learning
- Jump-Start Reinforcement Learning
- Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress
- Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient
- AWAC: Accelerating Online Reinforcement Learning with Offline Datasets
- Offline-to-Online Reinforcement Learning via Balanced Replay and Pessimistic Q-Ensemble
- Pre-Training for Robots: Offline RL Enables Learning New Tasks from a Handful of Trials
- MOTO: Offline to Online Fine-tuning for Model-Based Reinforcement Learning
- Launchpad: Learning to Schedule Using Offline and Online RL Methods
- Guiding Online Reinforcement Learning with Action-Free Offline Pretraining
- Efficient Online Reinforcement Learning with Offline Data
- Policy Expansion for Bridging Offline-to-Online Reinforcement Learning
- Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning
- Adaptive Policy Learning for Offline-to-Online Reinforcement Learning
- Finetuning from Offline Reinforcement Learning: Challenges, Trade-offs and Practical Solutions
- PROTO: Iterative Policy Regularized Offline-to-Online Reinforcement Learning
- Seizing Serendipity: Exploiting the Value of Past Success in Off-Policy Actor-Critic
- A Simple Unified Uncertainty-Guided Framework for Offline-to-Online Reinforcement Learning
- Sample Efficient Offline-to-Online Reinforcement Learning
- Guided Online Distillation: Promoting Safe Reinforcement Learning by Offline Demonstration
- Towards Robust Offline-to-Online Reinforcement Learning via Uncertainty and Smoothness
- Sample Efficient Reward Augmentation in offline-to-online Reinforcement Learning
- Adaptive Offline Data Replay in Offline-to-Online Reinforcement Learning
- Bayesian Offline-to-Online Reinforcement Learning : A Realist Approach
- SERA: Sample Efficient Reward Augmentation in offline-to-online Reinforcement Learning
- Offline Retraining for Online RL: Decoupled Policy Learning to Mitigate Exploration Bias - ->
- Planning to Go Out-of-Distribution in Offline-to-Online Reinforcement Learning
- Offline RL for Online RL: Decoupled Policy Learning for Mitigating Exploration Bias
- A Simple Unified Uncertainty-Guided Framework for Offline-to-Online Reinforcement Learning
- Uni-O4: Unifying Online and Offline Deep Reinforcement Learning with Multi-Step On-Policy Optimization
- Automatic Fine-Tuned Offline-to-Online Reinforcement Learning via Increased Simple Moving Average Q-value
- Guided Decoupled Exploration for Offline Reinforcement Learning Fine-tuning
- Offline Data Enhanced On-Policy Policy Gradient with Provable Guarantees
- Collaborative World Models: An Online-Offline Transfer RL Approach
- Don’t Start From Scratch: Leveraging Prior Data to Automate Robotic Reinforcement Learning - berkeley/)
- SUF: Stabilized Unconstrained Fine-Tuning for Offline-to-Online Reinforcement Learning
- A Perspective of Q-value Estimation on Offline-to-Online Reinforcement Learning
- Efficient and Stable Offline-to-online Reinforcement Learning via Continual Policy Revitalization
- ENOTO: Improving Offline-to-Online Reinforcement Learning with Q-Ensembles
- Bayesian Design Principles for Offline-to-Online Reinforcement Learning
- Energy-Guided Diffusion Sampling for Offline-to-Online Reinforcement Learning
- OLLIE: Imitation Learning from Offline Pretraining to Online Finetuning
- Offline-Boosted Actor-Critic: Adaptively Blending Optimal Historical Behaviors in Deep Off-Policy RL
- Improving Offline-to-Online Reinforcement Learning with Q Conditioned State Entropy Exploration
- Learning on the Job: Self-Rewarding Offline-to-Online Finetuning for Industrial Insertion of Novel Connectors from Vision
- Don’t Start From Scratch: Leveraging Prior Data to Automate Robotic Reinforcement Learning - berkeley/)
- MOTO: Offline to Online Fine-tuning for Model-Based Reinforcement Learning
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