Robust, Safe, and Certified Reinforcement Learning

Context and objectives of the research: Reinforcement learning (RL) is a fundamental technique that
enables agents to learn desirable behaviour through interactions with an unknown environment. Recent
years have witnessed outstanding advances in the field, and it has been applied with great success in a
variety of contexts ranging from traffic control and energy management to video games and autonomous
driving. However, it has been challenging to adopt RL in real-world safety-critical domains, such as
robotics and healthcare, because of its trial-and-error learning process that could lead to catastrophic
outcomes. As a result, the active field of safe RL has recently emerged, which investigates how safety can
be ensured both in deployment and during training. Safe RL promises to translate the recent advances in
RL into applications with tangible real-world impact.
The objective of this research project is to develop new algorithms and techniques for safe RL. We will
especially focus on model-based approaches and integrate them with techniques from formal verification to
provide safety guarantees. We also aim to incorporate recent advances in learning with non-Markovian
rewards, to model both complex temporal objectives and constraints.
Novelty of the research methodology: Different components of the proposed research have already been
explored to some extent in the literature. However, their combination especially with safe RL remains
underexplored. Furthermore, existing approaches mostly consider the problem from a verification perspective, whereas we aim to use RL as the foundation into which verification techniques are integrated.
This approach promises to improve the scalability of our techniques and thus make them applicable to
more realistic real-world problems.
Alignment to EPSRC research areas: The proposed project aligns with the research areas Artificial intelligence technologies and Verification and correctness.

AIMS's impact will be felt across domains of acute need within the UK. We expect AIMS to benefit: UK economic performance, through start-up creation; existing UK firms, both through research and addressing skills needs; UK health, by contributing to cancer research, and quality of life, through the delivery of autonomous vehicles; UK public understanding of and policy related to the transformational societal change engendered by autonomous systems.

Autonomous systems are acknowledged by essentially all stakeholders as important to the future UK economy. PwC claim that there is a £232 billion opportunity offered by AI to the UK economy by 2030 (10% of GDP). AIMS has an excellent track record of leadership in spinout creation, and will continue to foster the commercial projects of its students, through the provision of training in IP, licensing and entrepreneurship. With the help of Oxford Science Innovation (investment fund) and Oxford University Innovation (technology transfer office), student projects will be evaluated for commercial potential.

AIMS will also concretely contribute to UK economic competitiveness by meeting the UK's needs for experts in autonomous systems. To meet this need, AIMS will train cohorts with advanced skills that span the breadth of AI, machine learning, robotics, verification and sensor systems. The relevance of the training to the needs of industry will be ensured by the industrial partnerships at the heart of AIMS. These partnerships will also ensure that AIMS will produce research that directly targets UK industrial needs. Our partners span a wide range of UK sectors, including energy, transport, infrastructure, factory automation, finance, health, space and other extreme environments.

The autonomous systems that AIMS will enable also offer the prospect of epochal change in the UK's quality of life and health. As put by former Digital Secretary Matt Hancock, "whether it's improving travel, making banking easier or helping people live longer, AI is already revolutionising our economy and our society." AIMS will help to realise this potential through its delivery of trained experts and targeted research. In particular, two of the four Grand Challenge missions in the UK Industrial Strategy highlight the positive societal impact underpinned by autonomous systems. The "Artificial Intelligence and data" challenge has as its mission to "Use data, Artificial Intelligence and innovation to transform the prevention, early diagnosis and treatment of chronic diseases by 2030". To this mission, AIMS will contribute the outputs of its research pillar on cancer research. The "Future of mobility" challenge highlights the importance the autonomous vehicles will have in making transport "safer, cleaner and better connected." To this challenge, AIMS offers the world-leading research of its robotic systems research pillar.

AIMS will further promote the positive realisation of autonomous technologies through direct influence on policy. The world-leading academics amongst AIMS's supervisory pool are well-connected to policy formation e.g. Prof Osborne serving as a Commissioner on the Independent Commission on the Future of Work. Further, Dr Dan Mawson, Head of the Economy Unit; Economy and Strategic Analysis Team at BEIS will serve as an advisor to AIMS, ensuring bidirectional influence between policy objectives and AIMS research and training.

Broad understanding of autonomous systems is crucial in making a society robust to the transformations they will engender. AIMS will foster such understanding through its provision of opportunities for AIMS students to directly engage with the public. Given the broad societal importance of getting autonomous systems right, AIMS will deliver core training on the ethical, governance, economic and societal implications of autonomous systems.