Visualizing the World Model in an Agent's Value Function
Quick Answer
This study investigates whether model-free reinforcement learning, specifically Proximal Policy Optimization (PPO), learns an environmental model.
Quick Take
The findings reveal that the value function captures concepts like collectible rules, health impacts from food and water, and inventory heuristics, raising safety concerns for future AI development.
Key Points
- PPO agents were tested in the Crafter environment using counterfactual interventions.
- Value functions learned rules about collectible items like diamonds.
- Health impacts from food and water were recognized by the agents.
- Heuristics regarding inventory management emerged from the value function.
- The study highlights safety implications for advanced AI systems.
Paper Resources
📖 Reader Mode
~2 min readAbstract:Could model-free reinforcement learning algorithms actually be learning a model of the environment? In this work, we apply counterfactual interventions which hold everything about a state fixed, besides a targeted change, to explore if the value function of a Proximal Policy Optimization agent forms a world model of the Crafter environment. Despite only being trained to optimize reward, we discover that the value function learns concepts such as the rules governing when a diamond is collectible, water and food having an impact on health, heuristics regarding inventory, and more. Finally, we discuss the safety implications of our visualizations and some shortcomings of current reinforcement learning agents that will prevent us from building truly advanced machine intelligence.
| Comments: | 20 pages, 15 figues, 3 tables |
| Subjects: | Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.14186 [cs.AI] |
| (or arXiv:2610.14186v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.14186 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Shawheen Naderi [view email]
[v1]
Fri, 9 Oct 2026 18:01:55 UTC (11,537 KB)
— Originally published at arxiv.org
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