Maximum entropy exploration in contextual bandits with neural networks and energy based models

TL;DR

This paper introduces maximum entropy exploration techniques using neural networks and energy based models for contextual bandits, improving performance in static and dynamic environments with continuous and discrete actions.

Contribution

The paper presents novel maximum entropy exploration methods with neural networks and energy based models for contextual bandits, addressing uncertainty estimation issues in non-linear models.

Findings

Energy based models outperform standard algorithms.

Both techniques excel in static and dynamic environments.

Methods are effective for non-linear scenarios with continuous actions.

Abstract

Contextual bandits can solve a huge range of real-world problems. However, current popular algorithms to solve them either rely on linear models, or unreliable uncertainty estimation in non-linear models, which are required to deal with the exploration-exploitation trade-off. Inspired by theories of human cognition, we introduce novel techniques that use maximum entropy exploration, relying on neural networks to find optimal policies in settings with both continuous and discrete action spaces. We present two classes of models, one with neural networks as reward estimators, and the other with energy based models, which model the probability of obtaining an optimal reward given an action. We evaluate the performance of these models in static and dynamic contextual bandit simulation environments. We show that both techniques outperform well-known standard algorithms, where energy based models have the best overall performance. This provides practitioners with new techniques that perform well in static and dynamic settings, and are particularly well suited to non-linear scenarios with continuous action spaces.