Uni-O4: Unifying Online and Offline Deep Reinforcement Learning with Multi-Step On-Policy Optimization

• URL: http://arxiv.org/abs/2311.03351v4
• Date: Sun, 17 Mar 2024 04:40:06 GMT
• Title: Uni-O4: Unifying Online and Offline Deep Reinforcement Learning with Multi-Step On-Policy Optimization
• Authors: Kun Lei, Zhengmao He, Chenhao Lu, Kaizhe Hu, Yang Gao, Huazhe Xu,
• Abstract summary: Previous approaches treat offline and online learning as separate procedures, resulting in redundant designs and limited performance. We propose Uni-o4, which utilizes an on-policy objective for both offline and online learning. We demonstrate that our method achieves state-of-the-art performance in both offline and offline-to-online fine-tuning learning.
• Score: 24.969834057981046
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Combining offline and online reinforcement learning (RL) is crucial for efficient and safe learning. However, previous approaches treat offline and online learning as separate procedures, resulting in redundant designs and limited performance. We ask: Can we achieve straightforward yet effective offline and online learning without introducing extra conservatism or regularization? In this study, we propose Uni-o4, which utilizes an on-policy objective for both offline and online learning. Owning to the alignment of objectives in two phases, the RL agent can transfer between offline and online learning seamlessly. This property enhances the flexibility of the learning paradigm, allowing for arbitrary combinations of pretraining, fine-tuning, offline, and online learning. In the offline phase, specifically, Uni-o4 leverages diverse ensemble policies to address the mismatch issues between the estimated behavior policy and the offline dataset. Through a simple offline policy evaluation (OPE) approach, Uni-o4 can achieve multi-step policy improvement safely. We demonstrate that by employing the method above, the fusion of these two paradigms can yield superior offline initialization as well as stable and rapid online fine-tuning capabilities. Through real-world robot tasks, we highlight the benefits of this paradigm for rapid deployment in challenging, previously unseen real-world environments. Additionally, through comprehensive evaluations using numerous simulated benchmarks, we substantiate that our method achieves state-of-the-art performance in both offline and offline-to-online fine-tuning learning. Our website: https://lei-kun.github.io/uni-o4/ .

Related papers

• Bayesian Design Principles for Offline-to-Online Reinforcement Learning [50.97583504192167]
  offline-to-online fine-tuning is crucial for real-world applications where exploration can be costly or unsafe. In this paper, we tackle the dilemma of offline-to-online fine-tuning: if the agent remains pessimistic, it may fail to learn a better policy, while if it becomes optimistic directly, performance may suffer from a sudden drop. We show that Bayesian design principles are crucial in solving such a dilemma.
  arXiv  Detail & Related papers  (2024-05-31T16:31:07Z)
• Ensemble Successor Representations for Task Generalization in Offline-to-Online Reinforcement Learning [8.251711947874238]
  offline RL provides a promising solution by giving an offline policy, which can be refined through online interactions. Existing approaches perform offline and online learning in the same task, without considering the task generalization problem in offline-to-online adaptation. Our work builds upon the investigation of successor representations for task generalization in online RL and extends the framework to incorporate offline-to-online learning.
  arXiv  Detail & Related papers  (2024-05-12T08:52:52Z)
• Offline Retraining for Online RL: Decoupled Policy Learning to Mitigate Exploration Bias [96.14064037614942]
  offline retraining, a policy extraction step at the end of online fine-tuning, is proposed. An optimistic (exploration) policy is used to interact with the environment, and a separate pessimistic (exploitation) policy is trained on all the observed data for evaluation.
  arXiv  Detail & Related papers  (2023-10-12T17:50:09Z)
• ENOTO: Improving Offline-to-Online Reinforcement Learning with Q-Ensembles [52.34951901588738]
  We propose a novel framework called ENsemble-based Offline-To-Online (ENOTO) RL. By increasing the number of Q-networks, we seamlessly bridge offline pre-training and online fine-tuning without degrading performance. Experimental results demonstrate that ENOTO can substantially improve the training stability, learning efficiency, and final performance of existing offline RL methods.
  arXiv  Detail & Related papers  (2023-06-12T05:10:10Z)
• Finetuning from Offline Reinforcement Learning: Challenges, Trade-offs and Practical Solutions [30.050083797177706]
  offline reinforcement learning (RL) allows for the training of competent agents from offline datasets without any interaction with the environment. Online finetuning of such offline models can further improve performance. We show that it is possible to use standard online off-policy algorithms for faster improvement.
  arXiv  Detail & Related papers  (2023-03-30T14:08:31Z)
• Adaptive Policy Learning for Offline-to-Online Reinforcement Learning [27.80266207283246]
  We consider an offline-to-online setting where the agent is first learned from the offline dataset and then trained online. We propose a framework called Adaptive Policy Learning for effectively taking advantage of offline and online data.
  arXiv  Detail & Related papers  (2023-03-14T08:13:21Z)
• Adaptive Behavior Cloning Regularization for Stable Offline-to-Online Reinforcement Learning [80.25648265273155]
  Offline reinforcement learning, by learning from a fixed dataset, makes it possible to learn agent behaviors without interacting with the environment. During online fine-tuning, the performance of the pre-trained agent may collapse quickly due to the sudden distribution shift from offline to online data. We propose to adaptively weigh the behavior cloning loss during online fine-tuning based on the agent's performance and training stability. Experiments show that the proposed method yields state-of-the-art offline-to-online reinforcement learning performance on the popular D4RL benchmark.
  arXiv  Detail & Related papers  (2022-10-25T09:08:26Z)
• MOORe: Model-based Offline-to-Online Reinforcement Learning [26.10368749930102]
  We propose a model-based Offline-to-Online Reinforcement learning (MOORe) algorithm. Experiment results show that our algorithm smoothly transfers from offline to online stages while enabling sample-efficient online adaption.
  arXiv  Detail & Related papers  (2022-01-25T03:14:57Z)
• A Workflow for Offline Model-Free Robotic Reinforcement Learning [117.07743713715291]
  offline reinforcement learning (RL) enables learning control policies by utilizing only prior experience, without any online interaction. We develop a practical workflow for using offline RL analogous to the relatively well-understood for supervised learning problems. We demonstrate the efficacy of this workflow in producing effective policies without any online tuning.
  arXiv  Detail & Related papers  (2021-09-22T16:03:29Z)
• OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning [107.6943868812716]
  In many practical applications, the situation is reversed: an agent may have access to large amounts of undirected offline experience data, while access to the online environment is severely limited. Our main insight is that, when presented with offline data composed of a variety of behaviors, an effective way to leverage this data is to extract a continuous space of recurring and temporally extended primitive behaviors. In addition to benefiting offline policy optimization, we show that performing offline primitive learning in this way can also be leveraged for improving few-shot imitation learning.
  arXiv  Detail & Related papers  (2020-10-26T14:31:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.