Active Advantage-Aligned Online Reinforcement Learning with Offline Data

• URL: http://arxiv.org/abs/2502.07937v2
• Date: Fri, 30 May 2025 13:03:29 GMT
• Title: Active Advantage-Aligned Online Reinforcement Learning with Offline Data
• Authors: Xuefeng Liu, Hung T. C. Le, Siyu Chen, Rick Stevens, Zhuoran Yang, Matthew R. Walter, Yuxin Chen,
• Abstract summary: We introduce A3RL, which incorporates a novel confidence aware Active Advantage Aligned sampling strategy.<n>We demonstrate that our method outperforms competing online RL techniques that leverage offline data.
• Score: 56.98480620108727
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Online reinforcement learning (RL) enhances policies through direct interactions with the environment, but faces challenges related to sample efficiency. In contrast, offline RL leverages extensive pre-collected data to learn policies, but often produces suboptimal results due to limited data coverage. Recent efforts integrate offline and online RL in order to harness the advantages of both approaches. However, effectively combining online and offline RL remains challenging due to issues that include catastrophic forgetting, lack of robustness to data quality and limited sample efficiency in data utilization. In an effort to address these challenges, we introduce A3RL, which incorporates a novel confidence aware Active Advantage Aligned (A3) sampling strategy that dynamically prioritizes data aligned with the policy's evolving needs from both online and offline sources, optimizing policy improvement. Moreover, we provide theoretical insights into the effectiveness of our active sampling strategy and conduct diverse empirical experiments and ablation studies, demonstrating that our method outperforms competing online RL techniques that leverage offline data. Our code will be publicly available at:https://github.com/xuefeng-cs/A3RL.

Related papers

• MOORL: A Framework for Integrating Offline-Online Reinforcement Learning [6.7265073544042995]
  We propose Meta Offline-Online Reinforcement Learning (MOORL), a hybrid framework that unifies offline and online learning.<n>Our theoretical analysis demonstrates that the hybrid approach enhances exploration by effectively combining the complementary strengths of offline and online data.<n>With minimal computational overhead, MOORL achieves strong performance, underscoring its potential for practical applications in real-world scenarios.
  arXiv  Detail & Related papers  (2025-06-11T10:12:50Z)
• Efficient Online Reinforcement Learning Fine-Tuning Need Not Retain Offline Data [64.74333980417235]
  We show that retaining offline data is unnecessary as long as we use a properly-designed online RL approach for fine-tuning offline RL.<n>We show that Warm-start RL (WSRL) is able to fine-tune without retaining any offline data, and is able to learn faster and attains higher performance than existing algorithms.
  arXiv  Detail & Related papers  (2024-12-10T18:57:12Z)
• Offline-Boosted Actor-Critic: Adaptively Blending Optimal Historical Behaviors in Deep Off-Policy RL [42.57662196581823]
  Off-policy reinforcement learning (RL) has achieved notable success in tackling many complex real-world tasks. Most existing off-policy RL algorithms fail to maximally exploit the information in the replay buffer. We present Offline-Boosted Actor-Critic (OBAC), a model-free online RL framework that elegantly identifies the outperforming offline policy.
  arXiv  Detail & Related papers  (2024-05-28T18:38:46Z)
• 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)
• Reward-agnostic Fine-tuning: Provable Statistical Benefits of Hybrid Reinforcement Learning [66.43003402281659]
  A central question boils down to how to efficiently utilize online data collection to strengthen and complement the offline dataset. We design a three-stage hybrid RL algorithm that beats the best of both worlds -- pure offline RL and pure online RL. The proposed algorithm does not require any reward information during data collection.
  arXiv  Detail & Related papers  (2023-05-17T15:17:23Z)
• 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)
• Efficient Online Reinforcement Learning with Offline Data [78.92501185886569]
  We show that we can simply apply existing off-policy methods to leverage offline data when learning online. We extensively ablate these design choices, demonstrating the key factors that most affect performance. We see that correct application of these simple recommendations can provide a $mathbf2.5times$ improvement over existing approaches.
  arXiv  Detail & Related papers  (2023-02-06T17:30:22Z)
• Launchpad: Learning to Schedule Using Offline and Online RL Methods [9.488752723308954]
  Existing RL schedulers overlook the importance of learning from historical data and improving upon custom policies. offline reinforcement learning presents the prospect of policy optimization from pre-recorded datasets without online environment interaction. These methods address the challenges concerning the cost of data collection and safety, particularly pertinent to real-world applications of RL.
  arXiv  Detail & Related papers  (2022-12-01T16:40:11Z)
• 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)
• Behavioral Priors and Dynamics Models: Improving Performance and Domain Transfer in Offline RL [82.93243616342275]
  We introduce Offline Model-based RL with Adaptive Behavioral Priors (MABE) MABE is based on the finding that dynamics models, which support within-domain generalization, and behavioral priors, which support cross-domain generalization, are complementary. In experiments that require cross-domain generalization, we find that MABE outperforms prior methods.
  arXiv  Detail & Related papers  (2021-06-16T20:48:49Z)
• 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.