Dr Robert Loftin
School of Computer Science
Lecturer in Machine Learning
+44 114 215 7542
Full contact details
School of Computer Science
Regent Court (DCS)
211 Portobello
Sheffield
S1 4DP
- Profile
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Robert received his PhD in Computer Science from North Carolina State University in 2019, under the supervision of Dave Roberts. His dissertation examined the types of latent knowledge conveyed by human-provided feedback and demonstrations, and developed interactive learning algorithms which leverage models of human behavior to extract this information. He received his Bachelor's in Computer Science from Georgia Tech in 2011. After completing his PhD, he did a two-year post-doc with Microsoft Research Cambridge, exploring the use of Reinforcement Learning and Interactive Learning in commercial game development. He also completed a post-doc at TU Delft with Frans Oliehoek, where he focused on applying game theory and multi-agent reinforcement learning to human-AI cooperation. Robert is currently a Lecturer in Machine Learning at the University of Sheffield.
- Research interests
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- Ad Hoc Human-AI Cooperation
- Human-AI Alignment
- Human-in-the-loop Machine Learning
- Deep Reinforcement Learning
- Multi-Agent RL
- Human-Robot Interaction
- AI for Games
- Publications
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Journal articles
- Curriculum Design for Machine Learners in Sequential Decision Tasks. IEEE Transactions on Emerging Topics in Computational Intelligence, 2(4), 268-277.
- Learning behaviors via human-delivered discrete feedback: modeling implicit feedback strategies to speed up learning. Autonomous Agents and Multi-Agent Systems, 30(1), 30-59.
Conference proceedings papers
- Entropy Maximization in High Dimensional Multiagent State Spaces. 2023 International Symposium on Multi-Robot and Multi-Agent Systems (MRS), 4 December 2023 - 5 December 2023.
- Novelty Seeking Multiagent Evolutionary Reinforcement Learning. Proceedings of the Genetic and Evolutionary Computation Conference
- On the Impossibility of Learning to Cooperate with Adaptive Partner Strategies in Repeated Games. Proceedings of Machine Learning Research, Vol. 162 (pp 14197-14209)
- Strategically Efficient Exploration in Competitive Multi-agent Reinforcement Learning. Proceedings of Machine Learning Research, Vol. 161 (pp 1587-1596)
- Strategically Efficient Exploration in Competitive Multi-agent Reinforcement Learning. 37th Conference on Uncertainty in Artificial Intelligence, UAI 2021 (pp 1587-1596)
- Better exploration with optimistic actor-critic. Advances in Neural Information Processing Systems, Vol. 32
- Curriculum Design for Machine Learners in Sequential Decision Tasks. AAMAS'17: PROCEEDINGS OF THE 16TH INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS (pp 1682-1684)
- Interactive Learning from Policy-Dependent Human Feedback. INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 70, Vol. 70
- Improving developer participation rates in surveys. 2013 6th International Workshop on Cooperative and Human Aspects of Software Engineering (CHASE), 25 May 2013 - 25 May 2013.
- Uncoupled Learning of Differential Stackelberg Equilibria with Commitments. Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2024). Auckland, New Zealand, 6 May 2024 - 6 May 2024.
Preprints
- Uncoupled Learning of Differential Stackelberg Equilibria with
Commitments.
- Curriculum Design for Machine Learners in Sequential Decision Tasks. IEEE Transactions on Emerging Topics in Computational Intelligence, 2(4), 268-277.
- Research group
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Machine Learning research group