Meta-Learning Bandit Policies by Gradient Ascent

TL;DR

This paper introduces a meta-learning approach for bandit policies using gradient ascent on parameterized, differentiable policies, enabling high reward performance across diverse bandit instances with theoretical and empirical validation.

Contribution

It develops a novel framework for meta-learning bandit policies through differentiable parameterized policies optimized via policy gradients, bridging the gap between regret minimization and Bayesian approaches.

Findings

Gradient-based meta-learning improves bandit policy performance.

Differentiable policies with low regret are effective across various problems.

Baseline subtraction and learned biases enhance learning efficiency.

Abstract

Most bandit policies are designed to either minimize regret in any problem instance, making very few assumptions about the underlying environment, or in a Bayesian sense, assuming a prior distribution over environment parameters. The former are often too conservative in practical settings, while the latter require assumptions that are hard to verify in practice. We study bandit problems that fall between these two extremes, where the learning agent has access to sampled bandit instances from an unknown prior distribution $\mathcal{P}$ and aims to achieve high reward on average over the bandit instances drawn from $\mathcal{P}$. This setting is of a particular importance because it lays foundations for meta-learning of bandit policies and reflects more realistic assumptions in many practical domains. We propose the use of parameterized bandit policies that are differentiable and can be optimized using policy gradients. This provides a broadly applicable framework that is easy to implement. We derive reward gradients that reflect the structure of bandit problems and policies, for both non-contextual and contextual settings, and propose a number of interesting policies that are both differentiable and have low regret. Our algorithmic and theoretical contributions are supported by extensive experiments that show the importance of baseline subtraction, learned biases, and the practicality of our approach on a range problems.