Local policy search with Bayesian optimization

arXiv (Cornell University), vol. 34

Abstract

Reinforcement learning (RL) aims to find an optimal policy by interaction with an environment. Consequently, learning complex behavior requires a vast number of samples, which can be prohibitive in practice. Nevertheless, instead of systematically reasoning and actively choosing informative samples, policy gradients for local search are often obtained from random perturbations. These random samples yield high variance estimates and hence are sub-optimal in terms of sample complexity. Actively selecting informative samples is at the core of Bayesian optimization, which constructs a probabilistic surrogate of the objective from past samples to reason about informative subsequent ones. In this paper, we propose to join both worlds. We develop an algorithm utilizing a probabilistic model of the objective function and its gradient. Based on the model, the algorithm decides where to query a noisy zeroth-order oracle to improve the gradient estimates. The resulting algorithm is a novel type of policy search method, which we compare to existing black-box algorithms. The comparison reveals improved sample complexity and reduced variance in extensive empirical evaluations on synthetic objectives. Further, we highlight the benefits of active sampling on popular RL benchmarks.

Authors 3

1. Sarah Müller

   University of Tübingen · Max Planck Institute for Intelligent Systems

   Affiliation as printed

   Institute for Ophthalmic Research , University of Tübingen , Tübingen , Germany

   Max Planck Institute for Intelligent Systems , Stuttgart , Germany

2. Alexander von Rohr Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Aachen, Germany

3. Sebastian Trimpe Aachen Institute for Data Science in Mechanical Engineering

   RWTH Aachen University · Max Planck Institute for Intelligent Systems

   Affiliation as printed

   Institute for Data Science in Mechanical Engineering , RWTH Aachen University , Germany

   Max Planck Institute for Intelligent Systems , Stuttgart , Germany

   RWTH Aachen University, Aachen, Germany