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This effort is focused on examining the behavior of reinforcement learningsystems in personalization environments and detailing the differences in policyentropy associated with the type of learning algorithm utilized. We demonstratethat Policy Optimization agents often possess low-entropy policies duringtraining, which in practice results in agents prioritizing certain actions andavoiding others. Conversely, we also show that Q-Learning agents are far lesssusceptible to such behavior and generally maintain high-entropy policiesthroughout training, which is often preferable in real-world applications. Weprovide a wide range of numerical experiments as well as theoreticaljustification to show that these differences in entropy are due to the type oflearning being employed.

Examining Policy Entropy of Reinforcement Learning Agents for Personalization Tasks