World model in agent's value function discovered
Visualizing the World Model in an Agent's Value Function arXiv:2610.14186v1 Announce Type: new Abstract: 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…
1 source primary source
Key points
- Agents understand basic environmental rules like diamond collection
- Value functions reveal insights into environmental dynamics
Read the original at arXiv cs.AI · by Shawheen Naderi, Fatemeh Akherati, Yubei Chen primary sourceOpen source ↗
The headline, key points and digest above were generated by Digest AI's editorial model from the linked sources. Automated summaries can contain errors: the sources are the record. Spotted a mistake? Tell us. Published by Martin K., who runs Digest AI and handles corrections.
More in Research
All →- Asset learns to compress speech encoders · 1 src
- Researchers find Qwen3’s reasoning gains may stem from ‘thinking leakage’ · 1 src
- Researchers test AI agents improving LLM training strategies with up to 13.34% gains · 1 src
- Researchers model how AI and humans time new words in text · 1 src
- Researchers propose Next-Concept Modeling for faster text generation · 1 src
Comments
via GitHub Discussions