Energy-Guided Diffusion Sampling for Offline-to-Online Reinforcement Learning

• URL: http://arxiv.org/abs/2407.12448v2
• Date: Tue, 3 Sep 2024 18:40:47 GMT
• Title: Energy-Guided Diffusion Sampling for Offline-to-Online Reinforcement Learning
• Authors: Xu-Hui Liu, Tian-Shuo Liu, Shengyi Jiang, Ruifeng Chen, Zhilong Zhang, Xinwei Chen, Yang Yu,
• Abstract summary: We introduce textbfEnergy-guided textbfDIffusion textbfSampling (EDIS) EDIS uses a diffusion model to extract prior knowledge from the offline dataset and employs energy functions to distill this knowledge for enhanced data generation in the online phase. We observe a notable 20% average improvement in empirical performance on MuJoCo, AntMaze, and Adroit environments.
• Score: 13.802860320234469
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Combining offline and online reinforcement learning (RL) techniques is indeed crucial for achieving efficient and safe learning where data acquisition is expensive. Existing methods replay offline data directly in the online phase, resulting in a significant challenge of data distribution shift and subsequently causing inefficiency in online fine-tuning. To address this issue, we introduce an innovative approach, \textbf{E}nergy-guided \textbf{DI}ffusion \textbf{S}ampling (EDIS), which utilizes a diffusion model to extract prior knowledge from the offline dataset and employs energy functions to distill this knowledge for enhanced data generation in the online phase. The theoretical analysis demonstrates that EDIS exhibits reduced suboptimality compared to solely utilizing online data or directly reusing offline data. EDIS is a plug-in approach and can be combined with existing methods in offline-to-online RL setting. By implementing EDIS to off-the-shelf methods Cal-QL and IQL, we observe a notable 20% average improvement in empirical performance on MuJoCo, AntMaze, and Adroit environments. Code is available at \url{https://github.com/liuxhym/EDIS}.

Related papers

• D5RL: Diverse Datasets for Data-Driven Deep Reinforcement Learning [99.33607114541861]
  We propose a new benchmark for offline RL that focuses on realistic simulations of robotic manipulation and locomotion environments. Our proposed benchmark covers state-based and image-based domains, and supports both offline RL and online fine-tuning evaluation.
  arXiv  Detail & Related papers  (2024-08-15T22:27:00Z)
• ATraDiff: Accelerating Online Reinforcement Learning with Imaginary Trajectories [27.5648276335047]
  Training autonomous agents with sparse rewards is a long-standing problem in online reinforcement learning (RL) We propose a novel approach that leverages offline data to learn a generative diffusion model, coined as Adaptive Trajectory diffuser (ATraDiff) ATraDiff consistently achieves state-of-the-art performance across a variety of environments, with particularly pronounced improvements in complicated settings.
  arXiv  Detail & Related papers  (2024-06-06T17:58:15Z)
• Simple Ingredients for Offline Reinforcement Learning [86.1988266277766]
  offline reinforcement learning algorithms have proven effective on datasets highly connected to the target downstream task. We show that existing methods struggle with diverse data: their performance considerably deteriorates as data collected for related but different tasks is simply added to the offline buffer. We show that scale, more than algorithmic considerations, is the key factor influencing performance.
  arXiv  Detail & Related papers  (2024-03-19T18:57:53Z)
• DiffStitch: Boosting Offline Reinforcement Learning with Diffusion-based Trajectory Stitching [21.263554926053178]
  In offline reinforcement learning (RL), the performance of the learned policy highly depends on the quality of offline datasets. We introduce Diffusion-based Trajectory Stitching (DiffStitch), a novel diffusion-based data augmentation pipeline. DiffStitch effectively connects low-reward trajectories with high-reward trajectories, forming globally optimal trajectories to address the challenges faced by offline RL algorithms.
  arXiv  Detail & Related papers  (2024-02-04T10:30:23Z)
• 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)
• Dual Generator Offline Reinforcement Learning [90.05278061564198]
  In offline RL, constraining the learned policy to remain close to the data is essential. In practice, GAN-based offline RL methods have not performed as well as alternative approaches. We show that not only does having two generators enable an effective GAN-based offline RL method, but also approximates a support constraint.
  arXiv  Detail & Related papers  (2022-11-02T20:25:18Z)
• DARA: Dynamics-Aware Reward Augmentation in Offline Reinforcement Learning [17.664027379555183]
  offline reinforcement learning algorithms promise to be applicable in settings where a fixed dataset is available and no new experience can be acquired. This paper formulates the offline dynamics adaptation by using (source) offline data collected from another dynamics to relax the requirement for the extensive (target) offline data. With only modest amounts of target offline data, our performance consistently outperforms the prior offline RL methods in both simulated and real-world tasks.
  arXiv  Detail & Related papers  (2022-03-13T14:30:55Z)
• 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.