Bootstrap Off-policy with World Model

• URL: http://arxiv.org/abs/2511.00423v1
• Date: Sat, 01 Nov 2025 06:33:04 GMT
• Title: Bootstrap Off-policy with World Model
• Authors: Guojian Zhan, Likun Wang, Xiangteng Zhang, Jiaxin Gao, Masayoshi Tomizuka, Shengbo Eben Li,
• Abstract summary: We propose BOOM, a framework that tightly integrates planning and off-policy learning through a bootstrap loop.<n>BOOM achieves state-of-the-art results in both training stability and final performance.
• Score: 59.129118672069644
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Online planning has proven effective in reinforcement learning (RL) for improving sample efficiency and final performance. However, using planning for environment interaction inevitably introduces a divergence between the collected data and the policy's actual behaviors, degrading both model learning and policy improvement. To address this, we propose BOOM (Bootstrap Off-policy with WOrld Model), a framework that tightly integrates planning and off-policy learning through a bootstrap loop: the policy initializes the planner, and the planner refines actions to bootstrap the policy through behavior alignment. This loop is supported by a jointly learned world model, which enables the planner to simulate future trajectories and provides value targets to facilitate policy improvement. The core of BOOM is a likelihood-free alignment loss that bootstraps the policy using the planner's non-parametric action distribution, combined with a soft value-weighted mechanism that prioritizes high-return behaviors and mitigates variability in the planner's action quality within the replay buffer. Experiments on the high-dimensional DeepMind Control Suite and Humanoid-Bench show that BOOM achieves state-of-the-art results in both training stability and final performance. The code is accessible at https://github.com/molumitu/BOOM_MBRL.

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