Authors: Sreejan Kumar, Carlos G. Correa, Ishita Dasgupta, Raja Marjieh, Michael Hu, Robert D. Hawkins, Nathaniel Daw, Jonathan Cohen, Karthik R Narasimhan, Thomas L. Griffiths

Abstract: Strong inductive biases are a key component of human intelligence, allowing people to quickly learn a variety of tasks. Although meta-learning has emerged as an approach for endowing neural networks with useful inductive biases, agents trained by meta-learning may acquire very different strategies from humans. We show that co-training these agents on predicting representations from natural language task descriptions and from programs induced to generate such tasks guides them toward human-like inductive biases. Human-generated language descriptions and program induction with library learning both result in more human-like behavior in downstream meta-reinforcement learning agents than less abstract controls (synthetic language descriptions, program induction without library learning), suggesting that the abstraction supported by these representations is key.