Few-Shot Bayesian Imitation Learning with Logical Program Policies
This addresses the challenge of data scarcity in imitation learning for tasks with structured variation, such as strategy games, though it is incremental in combining existing techniques like DSLs and Bayesian methods.
The authors tackled the problem of learning policies from very few demonstrations by proposing a method that uses logical program policies and Bayesian inference, achieving 20-1,000x more data efficiency than convolutional methods and high computational efficiency compared to vanilla program induction.
Humans can learn many novel tasks from a very small number (1--5) of demonstrations, in stark contrast to the data requirements of nearly tabula rasa deep learning methods. We propose an expressive class of policies, a strong but general prior, and a learning algorithm that, together, can learn interesting policies from very few examples. We represent policies as logical combinations of programs drawn from a domain-specific language (DSL), define a prior over policies with a probabilistic grammar, and derive an approximate Bayesian inference algorithm to learn policies from demonstrations. In experiments, we study five strategy games played on a 2D grid with one shared DSL. After a few demonstrations of each game, the inferred policies generalize to new game instances that differ substantially from the demonstrations. Our policy learning is 20--1,000x more data efficient than convolutional and fully convolutional policy learning and many orders of magnitude more computationally efficient than vanilla program induction. We argue that the proposed method is an apt choice for tasks that have scarce training data and feature significant, structured variation between task instances.