Synthetic Environment Design for Reinforcement Learning

Lead Research Organisation: University of Oxford


Brief description of the context of the research including the potential impact
Reinforcement learning (RL) has made significant strides over the last decade, achieving superhuman performance on a range of RL tasks. However, its success has largely been limited to simulated environments where it is possible to generate task experience indefinitely, an unrealistic assumption for real-world tasks with limited available experience and poor simulation quality. One approach to improving performance in this domain is to learn a simulation of the environment, before training an agent on synthetic data generated from this simulation. Doing so would enable RL agents to be trained on significantly less experience and without a manually-programmed simulation, transferring these advancements to real-world tasks.

Aims and Objectives
We aim to develop various methods for generating synthetic task experience and training RL agents on synthetically generated data. These methods should allow for improvements in sample efficiency on a range of existing RL benchmarks and may enable novel applications of RL in new domains.

Novelty of the research methodology
Training RL agents on synthetic experience is a little-studied topic, however, some recent works have demonstrated success from it. We are proposing a novel approach to generating synthetic experience, in which we will use insights from Unsupervised Environment Design to tailor the experience to the current capabilities of the agent, thereby improving training efficiency and asymptotic performance.

Alignment to EPSRC's strategies and research areas
This project falls under the EPSRC's Artificial Intelligence Technologies research area, within the Engineering and ICT themes. Specifically, this work will enable the deployment of RL-based systems to real-world applications such as robotics, by allowing agents to train on minimal task experience and without any need for manually-programmed simulation.

Planned Impact

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.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S024050/1 01/10/2019 31/03/2028
2579054 Studentship EP/S024050/1 01/10/2021 30/09/2025 Matthew Jackson