Increasing Entropy to Boost Policy Gradient Performance on Personalization Tasks
Increasing Entropy to Boost Policy Gradient Performance on Personalization Tasks
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. Policy gradient agents are prone to entropy collapse, which means certain actions are seldomly, if ever, selected. We augment the optimization objective function …