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Reinforcement Learning (RL) has emerged as a fundamental tool for the design of artificial agents possessing optimality and safety guarantees. A series of results has established that the behaviour learnt by RL agents interacting with one another can be understood using the tools of evolutionary game theory – for example, one can investigate conditions under which agents’ learnt policies converge to Evolutionarily Stable Strategies of the underlying game. Issues such as the emergence of cooperation, the tragedy of the commons, partner selection, population structure, and the exploration/exploitation trade-off are key questions in the study of both artificial intelligence and evolutionary biology and can be described using similar underlying mathematical frameworks. The aim of this workshop is to explore recent developments at this interface, to discuss open questions, and to bring together researchers from both communities.

Following the success of the firstLink opens in a new windowLink opens in a new window, the second edition of the P@W Summer School investigates the large-scale behaviour and properties of statistical mechanics systems, which represent one of the most compelling drivers of modern research in Probability and its applications to Statistics, Data Science and Machine Learning. The aim of the school is to engage PhD students and early-career researchers with cutting-edge topics at the frontiers of current knowledge. It will feature four lecture courses by leading experts that will explore recent developments on different themes: stochastic PDEs and homogenisation, interacting particle systems, mixing and cut-off phenomena. The programme will be complemented by discussion and exercise sessions so to maximise interaction among participants and speakers.