Increasing Entropy to Boost Policy Gradient Performance on Personalization Tasks

1 code implementation • 9 Oct 2023 • Andrew Starnes, Anton Dereventsov, Clayton Webster

In this effort, we consider the impact of regularization on the diversity of actions taken by policies generated from reinforcement learning agents trained using a policy gradient.

Modeling Non-deterministic Human Behaviors in Discrete Food Choices

2 code implementations • 23 Jan 2023 • Andrew Starnes, Anton Dereventsov, E. Susanne Blazek, Folasade Phillips

We establish a non-deterministic model that predicts a user's food preferences from their demographic information.

Examining Policy Entropy of Reinforcement Learning Agents for Personalization Tasks

1 code implementation • 21 Nov 2022 • Anton Dereventsov, Andrew Starnes, Clayton G. Webster

This effort is focused on examining the behavior of reinforcement learning systems in personalization environments and detailing the differences in policy entropy associated with the type of learning algorithm utilized.

Q-Learning reinforcement-learning +1