ENOTO: Improving Offline-to-Online Reinforcement Learning with Q-Ensembles

• URL: http://arxiv.org/abs/2306.06871v4
• Date: Sun, 21 Jul 2024 14:49:35 GMT
• Title: ENOTO: Improving Offline-to-Online Reinforcement Learning with Q-Ensembles
• Authors: Kai Zhao, Jianye Hao, Yi Ma, Jinyi Liu, Yan Zheng, Zhaopeng Meng,
• Abstract summary: 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.
• Score: 52.34951901588738
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Offline reinforcement learning (RL) is a learning paradigm where an agent learns from a fixed dataset of experience. However, learning solely from a static dataset can limit the performance due to the lack of exploration. To overcome it, offline-to-online RL combines offline pre-training with online fine-tuning, which enables the agent to further refine its policy by interacting with the environment in real-time. Despite its benefits, existing offline-to-online RL methods suffer from performance degradation and slow improvement during the online phase. To tackle these challenges, 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. Moreover, to expedite online performance enhancement, we appropriately loosen the pessimism of Q-value estimation and incorporate ensemble-based exploration mechanisms into our framework. Experimental results demonstrate that ENOTO can substantially improve the training stability, learning efficiency, and final performance of existing offline RL methods during online fine-tuning on a range of locomotion and navigation tasks, significantly outperforming existing offline-to-online RL methods.

Related papers

• Active Advantage-Aligned Online Reinforcement Learning with Offline Data [56.98480620108727]
  A3 RL is a novel method that actively selects data from combined online and offline sources to optimize policy improvement. We provide theoretical guarantee that validates the effectiveness of our active sampling strategy.
  arXiv  Detail & Related papers  (2025-02-11T20:31:59Z)
• Optimistic Critic Reconstruction and Constrained Fine-Tuning for General Offline-to-Online RL [36.65926744075032]
  offline-to-online (O2O) reinforcement learning improves performance rapidly with limited online interactions. Recent studies often design fine-tuning strategies for a specific offline RL method and cannot perform general O2O learning from any offline method. We propose to handle these two mismatches simultaneously, which aims to achieve general O2O learning from any offline method to any online method.
  arXiv  Detail & Related papers  (2024-12-25T09:52:22Z)
• 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. 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)
• 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)
• 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 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)
• Offline-to-Online Reinforcement Learning via Balanced Replay and Pessimistic Q-Ensemble [135.6115462399788]
  Deep offline reinforcement learning has made it possible to train strong robotic agents from offline datasets. State-action distribution shift may lead to severe bootstrap error during fine-tuning. We propose a balanced replay scheme that prioritizes samples encountered online while also encouraging the use of near-on-policy samples.
  arXiv  Detail & Related papers  (2021-07-01T16:26:54Z)
• 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.