Meta Learning for Reinforcement Learning

Brief description of the context of the research including potential impact

Meta learning, or 'learning to learn', offers potential for us to move away from hand-crafted algorithms in machine learning. Based on the fact that learned algorithms thus far have proven more effective than hand-crafted features in practically all scenarios, it is only reasonable to expect that this holds in terms of a machine's learning process as well. This has the potential to drastically improve our machine learning models in practically all settings and possible enable us to work with ML algorithms in currently non-practical settings.

Aims and Objectives

The primary aim of our work is to learn effective algorithms for solving reinforcement learning tasks, though it is hoped that at least some of the work throughout this DPhil will be generalisable to other fields of machine learning too. In particular, my current project is focused on attempting to learn a more effective optimiser to replace handcrafted update rules that will, hopefully, generalise within reinforcement learning.

Novelty of the research methodology

There is a range of novelty on offer in this current work; we are exploring different methods for meta learning than the current SOTA (employing evolutionary strategies) and exploring the different effects of conditioning features on performance. This is also leading to some interesting analysis on the impact of a meta learned optimiser on, eg, plasticity loss in neural networks, particularly in reinforcement learning.

Alignment to EPSRC's strategies and research areas (which EPSRC research area the project relates to) Further information on the areas can be found on http://www.epsrc.ac.uk/research/ourportfolio/researchareas/ http://www.epsrc.ac.uk/research/ourportfolio/researchareas/)

This is closely aligned to pushing AI technologies, which is one of EPSRC's research areas.

Any companies or collaborators involved

Currently, Pablo Castro (Deepmind) is working with me on one of my projects.

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.