About this opportunity:
Reinforcement Learning (RL) is increasingly used for sequential decision-making in areas such as robotics, recommender systems, autonomous control, and telecommunications. In telecom, RL is being explored for radio resource allocation, traffic steering, energy saving, and network self-optimisation. However, RL policies are typically opaque, making it difficult for operators, engineers, and researchers to understand why an agent selected a particular action.
This is a serious obstacle to deployment in telecom, where trust, accountability, and the ability to diagnose misbehaviour are essential. Explainable Reinforcement Learning (XRL), a sub-field of Explainable AI (XAI), aims to make agent behaviour interpretable to humans.
This thesis will design and run a user study comparing Feature Importance (FI) explanations with Temporal Policy Decomposition (TPD), which explains actions through predicted future outcomes. The study will investigate whether outcome-based explanations are more useful to humans than feature-attribution explanations in an RL context.
The work corresponds to two students, 30 hp each, and can be organised into two subtracks. The students will collaborate on the user-study infrastructure and codebase. The location is Stockholm, Kista, and the preferred starting period is October 2026 to January 2027.
What you will do:
- Review XAI and XRL literature and identify appropriate metrics and evaluation protocols for explanation quality.
- Design a user-study protocol based on four conditions:
- No explanation: participants see only the agent's actions.
- FI only: participants see feature-importance explanations.
- TPD only: participants see temporal-outcome explanations.
- TPD + FI: participants see both explanation types.
- Extend an existing web application to support the required XRL methods, the combined condition, and new measurements.
- Run a pilot study, refine the protocol, recruit participants, and conduct the main user study.
- Analyse the results statistically and evaluate the effectiveness of each explanation method individually and in combination.
- Write the thesis report and present the results to the research team.
The skills you bring:
- You are a Master's student in Computer Science, Human-Machine Interaction, Machine Learning, Data Science, or a related field.
- You have a foundation in machine learning, basic statistics, and data analysis.
- You have good programming skills in JavaScript and Python.
- You have good English proficiency and can communicate your findings clearly.
Skills Required
- Currently pursuing a Master's degree in Computer Science, Human-Machine Interaction, Machine Learning, Data Science, or a related field
- Foundation in machine learning
- Basic statistics knowledge
- Data analysis knowledge
- Good programming skills in JavaScript
- Good programming skills in Python
- Good English proficiency
- Ability to communicate findings clearly
What the Team is Saying
.png)
.png)
What We Do
Ericsson builds the digital connectivity the world relies on. Our technology underpins the mobile networks, platforms, and systems that billions of people, businesses, and societies depend on every day. We are a global leader in communications technology, delivering mobile network infrastructure, cloud software, and wireless connectivity solutions for service providers and enterprises worldwide. Our networks support connectivity across 180+ countries, helping power everyday communication as well as critical digital services at global scale. Connectivity has evolved far beyond consumer mobile use. Today, nearly 80% of the world’s population accesses the internet via mobile networks, and Ericsson is helping shape what comes next. We are advancing 5G and 5G Advanced, developing network APIs that open connectivity to the global developer ecosystem, and applying automation and AI to make networks more intelligent, efficient, and resilient. Ericsson was the first company to launch live 5G networks on five continents, and our 5G platform is now commercially live in 150+ networks across 60+ countries. We also support more than 36,000 enterprise customers, enabling secure, high-performance connectivity for industries such as manufacturing, aviation, logistics, utilities, and public safety, where reliability and performance are mission critical. Innovation is central to how we work. Ericsson has approximately 28,000 employees in research and development, backed by one of the strongest intellectual property portfolios in the industry with 60,000+ granted patents. Our engineers, researchers, and technologists work across 100+ global R&D sites, helping define how networks evolve and how digital infrastructure is built for the long term. As the world moves toward a mobile-first, AI-powered, and cloud-driven future, connectivity becomes the foundation for digital transformation across every industry. Ericsson is building that foundation, shaping the future of digital connectivity through technology that operates at global scale and supports real-world impact, today and for what comes next.
Why Work With Us
Ericsson is a place for people who want to work on technology that powers everyday life. You’ll contribute to large-scale systems used every day, tackle complex challenges in live environments, and keep developing your skills and career in your own vision.
Gallery
.jpg)
.jpg)
.jpg)
.jpg)
.jpg)
Ericsson Offices
Hybrid Workspace
Employees engage in a combination of remote and on-site work.
Ericsson adopts a hybrid work model globally because we know balance matters. Sometimes things are better in real life. Other times we can be more productive at home. Our hybrid approach gives you the best of both worlds.
