Policy Gradient Optimization of Thompson Sampling Policies

Abstract

We study the use of policy gradient algorithms to optimize over a class of generalized Thompson sampling policies. Our central insight is to view the posterior parameter sampled by Thompson sampling as a kind of pseudo-action. Policy gradient methods can then be tractably applied to search over a class of sampling policies, which determine a probability distribution over pseudo-actions (i.e., sampled parameters) as a function of observed data. We also propose and compare policy gradient estimators that are specialized to Bayesian bandit problems. Numerical experiments demonstrate that direct policy search on top of Thompson sampling automatically corrects for some of the algorithm's known shortcomings and offers meaningful improvements even in long horizon problems where standard Thompson sampling is extremely effective.

References in corpus (6)

• Adam: A Method for Stochastic Optimization
• Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
• RL${}^2$: Fast Reinforcement Learning via Slow Reinforcement Learning
• Differentiable Bandit Exploration
• Meta-Learning Bandit Policies by Gradient Ascent
• Thompson Sampling with Information Relaxation Penalties

Cited by in corpus (3)

• Meta-Learning Bandit Policies by Gradient Ascent
• Meta-Thompson Sampling
• No Regrets for Learning the Prior in Bandits