Objects matter: object-centric world models improve reinforcement learning in visually complex environments

• URL: http://arxiv.org/abs/2501.16443v1
• Date: Mon, 27 Jan 2025 19:07:06 GMT
• Title: Objects matter: object-centric world models improve reinforcement learning in visually complex environments
• Authors: Weipu Zhang, Adam Jelley, Trevor McInroe, Amos Storkey,
• Abstract summary: We propose an object-centric reinforcement learning pipeline for visually complex games. We show how this pipeline can overcome the limitations of traditional model-based reinforcement learning.
• Score: 2.2532647717990937
• License:
• Abstract: Deep reinforcement learning has achieved remarkable success in learning control policies from pixels across a wide range of tasks, yet its application remains hindered by low sample efficiency, requiring significantly more environment interactions than humans to reach comparable performance. Model-based reinforcement learning (MBRL) offers a solution by leveraging learnt world models to generate simulated experience, thereby improving sample efficiency. However, in visually complex environments, small or dynamic elements can be critical for decision-making. Yet, traditional MBRL methods in pixel-based environments typically rely on auto-encoding with an $L_2$ loss, which is dominated by large areas and often fails to capture decision-relevant details. To address these limitations, we propose an object-centric MBRL pipeline, which integrates recent advances in computer vision to allow agents to focus on key decision-related elements. Our approach consists of four main steps: (1) annotating key objects related to rewards and goals with segmentation masks, (2) extracting object features using a pre-trained, frozen foundation vision model, (3) incorporating these object features with the raw observations to predict environmental dynamics, and (4) training the policy using imagined trajectories generated by this object-centric world model. Building on the efficient MBRL algorithm STORM, we call this pipeline OC-STORM. We demonstrate OC-STORM's practical value in overcoming the limitations of conventional MBRL approaches on both Atari games and the visually complex game Hollow Knight.

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