A Review Of Robust Agents Learn Causal World Models Cognitive Spirals

By switzerlandersing On Sep 14, 2025

Figure 3 From Robust Agents Learn Causal World Models | Semantic Scholar

Figure 3 From Robust Agents Learn Causal World Models | Semantic Scholar It has long been hypothesised that causal reasoning plays a fundamental role in robust and general intelligence. however, it is not known if agents must learn causal models in order to generalise to new domains, or if other inductive biases are sufficient. This paper by jonathan richens and tom everitt from google deepmind provides a formal theoretical answer to a critical question: do agents necessarily learn causal models to achieve robust generalization across different domains?.

Figure 4 From Robust Agents Learn Causal World Models | Semantic Scholar

Figure 4 From Robust Agents Learn Causal World Models | Semantic Scholar Theorem: assume agent satisfies regret bound for all local* interventions σ on any variable v. then we can learn an approximation of the underlying causal bayesian network (cbn) from the agent’s policy. It has long been hypothesised that causal reasoning plays a fundamental role in robust and general intelligence. however, it is not known if agents must learn causal models in order to generalise to new domains, or if other inductive biases are suficient. The paper signifies a real advancement in the selection theorem agenda, proving that it is possible to derive an implicit behavioral causal world model from policy oracles (agents) with low regret by appropriately querying them. The iclr logo above may be used on presentations. right click and choose download. it is a vector graphic and may be used at any scale.

Figure 5 From Robust Agents Learn Causal World Models | Semantic Scholar

Figure 5 From Robust Agents Learn Causal World Models | Semantic Scholar The paper signifies a real advancement in the selection theorem agenda, proving that it is possible to derive an implicit behavioral causal world model from policy oracles (agents) with low regret by appropriately querying them. The iclr logo above may be used on presentations. right click and choose download. it is a vector graphic and may be used at any scale. The paper explores a fundamental question in the field of artificial intelligence: does an agent need to learn a causal model of the world in order to be able to generalize and perform well in new situations, or can it get by with other types of inductive biases?. Agents trained to minimise a loss function across many domains are forced to learn a causal world model, which could enable them to reason about interventions, to counterfact, and to optimise a much larger set of objective functions. This paper shows that agents with low regret under these shifts possess an approximate causal model, raising implications for transfer learning and causal inference. It has long been hypothesised that causal reasoning plays a fundamental role in robust and general intelligence. however, it is not known if agents must learn causal models in order to generalise under distributional shifts, or if other inductive biases are sufficient.

An Illustrated Summary Of "Robust Agents Learn Causal World Model" — AI Alignment Forum

An Illustrated Summary Of "Robust Agents Learn Causal World Model" — AI Alignment Forum The paper explores a fundamental question in the field of artificial intelligence: does an agent need to learn a causal model of the world in order to be able to generalize and perform well in new situations, or can it get by with other types of inductive biases?. Agents trained to minimise a loss function across many domains are forced to learn a causal world model, which could enable them to reason about interventions, to counterfact, and to optimise a much larger set of objective functions. This paper shows that agents with low regret under these shifts possess an approximate causal model, raising implications for transfer learning and causal inference. It has long been hypothesised that causal reasoning plays a fundamental role in robust and general intelligence. however, it is not known if agents must learn causal models in order to generalise under distributional shifts, or if other inductive biases are sufficient.

An Illustrated Summary Of "Robust Agents Learn Causal World Model" — AI Alignment Forum

An Illustrated Summary Of "Robust Agents Learn Causal World Model" — AI Alignment Forum This paper shows that agents with low regret under these shifts possess an approximate causal model, raising implications for transfer learning and causal inference. It has long been hypothesised that causal reasoning plays a fundamental role in robust and general intelligence. however, it is not known if agents must learn causal models in order to generalise under distributional shifts, or if other inductive biases are sufficient.