WOMBET: World Model-based Experience Transfer for Robust and Sample-efficient Reinforcement Learning

• URL: http://arxiv.org/abs/2604.08958v2
• Date: Wed, 15 Apr 2026 07:14:50 GMT
• Title: WOMBET: World Model-based Experience Transfer for Robust and Sample-efficient Reinforcement Learning
• Abstract summary: Reinforcement learning in robotics is often limited by the cost and risk of data collection.<n>We propose textitWorld Model-based Experience Transfer (WOMBET), a framework that jointly generates and utilizes prior data.
• Score: 8.729531978655737
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reinforcement learning (RL) in robotics is often limited by the cost and risk of data collection, motivating experience transfer from a source task to a target task. Offline-to-online RL leverages prior data but typically assumes a given fixed dataset and does not address how to generate reliable data for transfer. We propose \textit{World Model-based Experience Transfer} (WOMBET), a framework that jointly generates and utilizes prior data. WOMBET learns a world model in the source task and generates offline data via uncertainty-penalized planning, followed by filtering trajectories with high return and low epistemic uncertainty. It then performs online fine-tuning in the target task using adaptive sampling between offline and online data, enabling a stable transition from prior-driven initialization to task-specific adaptation. We show that the uncertainty-penalized objective provides a lower bound on the true return and derive a finite-sample error decomposition capturing distribution mismatch and approximation error. Empirically, WOMBET improves sample efficiency and final performance over strong baselines on continuous control benchmarks, demonstrating the benefit of jointly optimizing data generation and transfer.

Related papers

• Continual Learning with Elastic Regularization and Synthetic Replay for Federated MLLM Fine-Tuning [15.445619998280243]
  Federated Continual Multimodal Learning (FedCMM) is a framework that embeds continual-learning safeguards into the federated optimization loop.<n>FedCMM consistently outperforms recent baselines on accuracy and backward transfer.
  arXiv  Detail & Related papers  (2026-07-13T19:40:30Z)
• Large Language Model-Empowered Decision Transformer for UAV-Enabled Data Collection [71.84636717632206]
  Unmanned aerial vehicles (UAVs) for reliable and energy-efficient data collection from spatially distributed devices holds great promise in supporting Internet of Things (IoT) applications.<n>We propose a joint language model (LLM) to learn effective UAV control policies.<n>LLM-CRDT outperforms benchmark online and offline methods, achieving up to 36.7% higher energy efficiency than current state-of-the-art DT approaches.
  arXiv  Detail & Related papers  (2025-09-17T13:05:08Z)
• Distributionally Robust Optimization with Adversarial Data Contamination [49.89480853499918]
  We focus on optimizing Wasserstein-1 DRO objectives for generalized linear models with convex Lipschitz loss functions.<n>Our primary contribution lies in a novel modeling framework that integrates robustness against training data contamination with robustness against distributional shifts.<n>This work establishes the first rigorous guarantees, supported by efficient computation, for learning under the dual challenges of data contamination and distributional shifts.
  arXiv  Detail & Related papers  (2025-07-14T18:34:10Z)
• Mixed-Sample SGD: an End-to-end Analysis of Supervised Transfer Learning [6.614418593039343]
  We consider the problem of designing an SGD procedure that alternates sampling between source and target data.<n>A main algorithmic difficulty is in understanding how to design such an adaptive sub-sampling mechanism at each SGD step.<n>We show that, such a mixed-sample SGD procedure is feasible for general prediction tasks with convex losses.
  arXiv  Detail & Related papers  (2025-07-06T00:03:34Z)
• Model-Based Offline Reinforcement Learning with Adversarial Data Augmentation [36.9134885948595]
  We introduce Model-based Offline Reinforcement learning with AdversariaL data augmentation.<n>In MORAL, we replace the fixed horizon rollout by employing adversaria data augmentation to execute alternating sampling with ensemble models.<n>Experiments on D4RL benchmark demonstrate that MORAL outperforms other model-based offline RL methods in terms of policy learning and sample efficiency.
  arXiv  Detail & Related papers  (2025-03-26T07:24:34Z)
• Goal-Conditioned Data Augmentation for Offline Reinforcement Learning [9.181158786602085]
  We introduce Goal-cOnditioned Data Augmentation (GODA), a goal-conditioned diffusion-based method for augmenting samples with higher quality.<n>GODA learns a comprehensive distribution representation of the original offline datasets while generating new data with selectively higher-return goals.<n>We conduct experiments on the D4RL benchmark and real-world challenges, specifically traffic signal control (TSC) tasks, to demonstrate GODA's effectiveness.
  arXiv  Detail & Related papers  (2024-12-29T16:42:30Z)
• Learn from the Learnt: Source-Free Active Domain Adaptation via Contrastive Sampling and Visual Persistence [60.37934652213881]
  Domain Adaptation (DA) facilitates knowledge transfer from a source domain to a related target domain. This paper investigates a practical DA paradigm, namely Source data-Free Active Domain Adaptation (SFADA), where source data becomes inaccessible during adaptation. We present learn from the learnt (LFTL), a novel paradigm for SFADA to leverage the learnt knowledge from the source pretrained model and actively iterated models without extra overhead.
  arXiv  Detail & Related papers  (2024-07-26T17:51:58Z)
• Federated Learning with Projected Trajectory Regularization [65.6266768678291]
  Federated learning enables joint training of machine learning models from distributed clients without sharing their local data. One key challenge in federated learning is to handle non-identically distributed data across the clients. We propose a novel federated learning framework with projected trajectory regularization (FedPTR) for tackling the data issue.
  arXiv  Detail & Related papers  (2023-12-22T02:12:08Z)
• Analysis and Optimization of Wireless Federated Learning with Data Heterogeneity [72.85248553787538]
  This paper focuses on performance analysis and optimization for wireless FL, considering data heterogeneity, combined with wireless resource allocation. We formulate the loss function minimization problem, under constraints on long-term energy consumption and latency, and jointly optimize client scheduling, resource allocation, and the number of local training epochs (CRE) Experiments on real-world datasets demonstrate that the proposed algorithm outperforms other benchmarks in terms of the learning accuracy and energy consumption.
  arXiv  Detail & Related papers  (2023-08-04T04:18:01Z)
• Towards Accurate Knowledge Transfer via Target-awareness Representation Disentanglement [56.40587594647692]
  We propose a novel transfer learning algorithm, introducing the idea of Target-awareness REpresentation Disentanglement (TRED) TRED disentangles the relevant knowledge with respect to the target task from the original source model and used as a regularizer during fine-tuning the target model. Experiments on various real world datasets show that our method stably improves the standard fine-tuning by more than 2% in average.
  arXiv  Detail & Related papers  (2020-10-16T17:45:08Z)

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.