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. We demonstrate that Policy Optimization agents often possess low-entropy policies during training, which in practice results in agents prioritizing certain actions and avoiding others. Conversely, we also show that Q-Learning agents are far less susceptible to such behavior and generally maintain high-entropy policies throughout training, which is often preferable in real-world applications. We provide a wide range of numerical experiments as well as theoretical justification to show that these differences in entropy are due to the type of learning being employed.

翻译：本研究聚焦于强化学习系统在个性化环境中的行为分析，详细阐述了与所用学习算法类型相关的策略熵差异。我们证明，策略优化智能体在训练过程中通常具有低熵策略，实际表现为智能体优先选择某些动作而回避其他动作。与之相对，我们还表明Q学习智能体对此类行为的敏感度显著降低，且在训练过程中通常保持高熵策略，这在实际应用中往往更具优势。通过大量数值实验及理论论证，我们证实这些熵值差异源于所采用的学习类型。