Reinforcement Learning (RL) is used extensively in Autonomous Systems (AS) as it enables learning at runtime without the need for a model of the environment or predefined actions. However, most applications of RL in AS, such as those based on Q-learning, can only optimize one objective, making it necessary in multi-objective systems to combine multiple objectives in a single objective function with predefined weights. A number of Multi-Objective Reinforcement Learning (MORL) techniques exist but they have mostly been applied in RL benchmarks rather than real-world AS systems. In this work, we use a MORL technique called Deep W-Learning (DWN) and apply it to the Emergent Web Servers exemplar, a self-adaptive server, to find the optimal configuration for runtime performance optimization. We compare DWN to two single-objective optimization implementations: {\epsilon}-greedy algorithm and Deep Q-Networks. Our initial evaluation shows that DWN optimizes multiple objectives simultaneously with similar results than DQN and {\epsilon}-greedy approaches, having a better performance for some metrics, and avoids issues associated with combining multiple objectives into a single utility function.

本研究解决了自主系统中应用强化学习时只能优化单一目标的问题，提出了一种称为深度W学习的多目标强化学习技术，通过在自适应网络服务器样本上进行性能优化评估，展示了其同时优化多个目标的能力。研究表明，深度W学习在某些指标上表现优于传统的单目标优化方法，避免了将多个目标组合为单一效用函数带来的问题。

多目标深度强化学习在自主系统优化中的应用