# Multi-Objective Deep Reinforcement Learning for Optimisation in Autonomous Systems

arXiv: https://arxiv.org/abs/2408.01188 · HTML: https://mlsanity.com/paper/2408.01188/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods.
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 2 Aug 2024.

## Placed in

- Deep reinforcement learning methods / [RL for games and combinatorial optimization](https://mlsanity.com/deeprl/rl-for-games-and-combinatorial-optimization/)

## Builds on (most cited first, 5 of 5)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Human-Centric Aware UAV Trajectory Planning in Search and Rescue Missions Employing Multi-Objective Reinforcement Learning with AHP and Similarity-Based Experience Replay](https://mlsanity.com/paper/2402.18487/) (2024)
- [A Comparison of Reinforcement Learning Techniques for Fuzzy Cloud Auto-Scaling](https://mlsanity.com/paper/1705.07114/) (2017)
- [Planning as Optimization: Dynamically Discovering Optimal Configurations for Runtime Situations](https://mlsanity.com/paper/1905.01071/) (2019)
- [Deep W-Networks: Solving Multi-Objective Optimisation Problems With Deep Reinforcement Learning](https://mlsanity.com/paper/2211.04813/) (2022)

## Built on by (most cited first, 2 of 2)

- [Auction-Based Online Policy Adaptation for Evolving Objectives](https://mlsanity.com/paper/2604.02151/) (2026)
- [Explaining Reinforcement Learning Decisions in Self-adaptive Systems](https://mlsanity.com/paper/2608.14620/) (2026)

## Abstract (arXiv)

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: ε-greedy algorithm and Deep Q-Networks. Our initial evaluation shows that DWN optimizes multiple objectives simultaneously with similar results than DQN and ε-greedy approaches, having a better performance for some metrics, and avoids issues associated with combining multiple objectives into a single utility function.