This AI entrepreneur is developing agents that can plan ahead for the unexpected
Model-based RL agents that plan without trial-and-error training could eventually affect autonomous systems policy - but this item is a researcher profile, not a policy or governance development.
Key points
- Researcher Danijar Hafner develops model-based reinforcement learning agents capable of planning ahead without trial-and-error.
- World models allow agents to simulate and imagine future outcomes, enabling complex real-world task execution.
- Profile piece on a single researcher - limited direct governance or policy relevance for APS practitioners.
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"This AI entrepreneur is developing agents that can plan ahead for the unexpected"
Source: MIT Technology Review – AI
Published: 8 September 2026
URL: https://www.technologyreview.com/2026/09/08/1142088/danijar-hafner-developing-plan-ahead-agents/
MIT Technology Review profiles Danijar Hafner, a researcher at Google DeepMind developing world models - AI systems that emulate physical reality - to train agents capable of planning ahead for unexpected situations. His model-based reinforcement learning approach allows agents and robots to execute complex tasks without real-world trial-and-error by imagining future outcomes within a simulation. The piece is biographical and technical in nature, tracing Hafner's background from rural Germany through internships at Google Brain and DeepMind.
Retrieved from SIMS, 15 November 2026.