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Evidence record 11598 · automatically gathered

Concept-Guided Spatial Regularization for World Models in Atari Pong

World models are usually evaluated as components of model-based reinforcement learning (MBRL) systems, while the world models themselves are rarely studied in isolation. We examine five representative visual world-model agents in Atari Pong: DreamerV3, DIAMOND, TWISTER, Simulus, and STORM. After reproducing their training pipelines and matching the reported agent performance, we freeze the learned world models and evaluate them with a closed-loop rollout diagnostic: a policy trained separately f

Record details

Published: 16 July 2026
Source: arXiv cs.AI
Category: Research
Topics: Regulation · Healthcare · Agents & autonomy
Retrieved: 18 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Shared topic: Regulation · Shared topic: Healthcare

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How to cite this record

ethics.ai (16 July 2026), “Concept-Guided Spatial Regularization for World Models in Atari Pong,” evidence record 11598, https://ethics.ai/record/11598 (originally published by arXiv cs.AI).

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