Bayesian approaches developed to solve the optimal design of sequential experiments are mathematically elegant but computationally challenging. Recently, techniques using amortization have been proposed to make these Bayesian approaches practical, by training a parameterized policy that proposes designs efficiently at deployment time. However, these methods may not sufficiently explore the design space, require access to a differentiable probabilistic model and can only optimize over continuous design spaces. Here, we address these limitations by showing that the problem of optimizing policies can be reduced to solving a Markov decision process (MDP). We solve the equivalent MDP with modern deep reinforcement learning techniques. Our experiments show that our approach is also computationally efficient at deployment time and exhibits state-of-the-art performance on both continuous and discrete design spaces, even when the probabilistic model is a black box.

Reinforcement learning has been very successful at learning control policies for robotic agents in order to perform various tasks, such as driving around a track, navigating a maze, and bipedal locomotion. One significant drawback of reinforcement learning methods is that they require a large number of data points in order to learn good policies, a trait known as poor data efficiency or poor sample efficiency. One approach for improving sample efficiency is supervised pre-training of policies to directly clone the behavior of an expert, but this suffers from poor generalization far from the training data. We propose to improve this by using Gaussian dropout networks with a regularization term based on variational inference in the pre-training step. We show that this initializes policy parameters to significantly better values than standard supervised learning or random initialization, thus greatly reducing sample complexity compared with state-of-the-art methods, and enabling an RL algorithm to learn optimal policies for high-dimensional continuous control problems in a practical time frame.