Probabilistic program inference often involves choices between various strategies. Rather than try to make the choices in advance or delegate them to the user, we can use reinforcement learning to try different strategies and see which performs well. When a compositional inference process is being used, we get a network of reinforcement learners. In our approach, the solution to an inference task is represented as a stream of successive approximations. We present strategies for choosing between a fixed set of such streams, for combining multiple streams to produce a single output stream, and for merging a stream of streams into a single stream.
Extended abstract: Reinforcement Learning for Inference