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Found 7 papers, 4 papers with code
Program-Based Strategy Induction for Reinforcement Learning
Typical models of learning assume incremental estimation of continuously-varying decision variables like expected rewards.
Exploring the hierarchical structure of human plans via program generation
We find that humans are sensitive to both metrics, but that both accounts fail to predict a qualitative feature of human-created programs, namely that people prefer programs with reuse over and above the predictions of MDL.
Humans decompose tasks by trading off utility and computational cost
Human behavior emerges from planning over elaborate decompositions of tasks into goals, subgoals, and low-level actions.
Using Natural Language and Program Abstractions to Instill Human Inductive Biases in Machines
Co-training on these representations result in more human-like behavior in downstream meta-reinforcement learning agents than less abstract controls (synthetic language descriptions, program induction without learned primitives), suggesting that the abstraction supported by these representations is key.
Disentangling Abstraction from Statistical Pattern Matching in Human and Machine Learning
However, because neural networks are hard to interpret, it can be difficult to tell whether agents have learned the underlying abstraction, or alternatively statistical patterns that are characteristic of that abstraction.
Meta-Learning of Structured Task Distributions in Humans and Machines
We then introduce a novel approach to constructing a "null task distribution" with the same statistical complexity as this structured task distribution but without the explicit rule-based structure used to generate the structured task.
Environmental statistics and the trade-off between model-based and TD learning in humans
There is much evidence that humans and other animals utilize a combination of model-based and model-free RL methods.
