< Back Dr Michelangelo Conserva Queen Mary University of London iGGi Alum Michelangelo Conserva is a second year PhD researcher studying principled exploration strategies in reinforcement learning. He is particularly interested in randomized exploration and, more generally, Bayesian methods for reinforcement learning. He holds a BSc in Statistics, Economics and Finance from Sapienza, University of Rome and an MSc in Computational Statistics and Machine learning from University College of London. A description of Michelangelo's research: As a PhD student at Queen Mary University of London, Michelangelo aims to leverage Bayesian models to develop principled algorithms for reinforcement learning in the context of function approximations. The main challenge lies in finding a balance between computational costs and optimality. Evaluating such balance requires careful evaluation, which is currently lacking in reinforcement learning. Email m.conserva@qmul.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisors: Prof. Simon Lucas Dr Paulo Rauber Featured Publication(s): Exploration with Foundation Models: Capabilities, Limitations, and Hybrid Approaches ForestCast: Forecasting Deforestation Risk at Scale with Deep Learning Foundation Models as World Models: A Foundational Study in Text-Based GridWorlds Heterogeneous graph neural networks for species distribution modeling Mapping Farmed Landscapes from Remote Sensing On the Limits of Tabular Hardness Metrics for Deep RL: A Study with the Pharos Benchmark What are you looking at? Team fight prediction through player camera Posterior Sampling for Deep Reinforcement Learning Hardness in Markov Decision Processes: Theory and Practice Recurrent Neural-Linear Posterior Sampling for Nonstationary Contextual Bandits The Graph Cut Kernel for Ranked Data Themes Game AI - Previous Next

< Back Studying General Agents in Video Games from the Perspective of Player Experience Link Author(s) C Guerrero-Romero, S Kumari, D Perez-Liebana, S Deterding Abstract More info TBA Link

< Back Robust Imitation Learning for Automated Game Testing Link Author(s) PV Amadori, T Bradley, R Spick, G Moss Abstract More info TBA Link

< Back League of Legends: A Study of Early Game Impact Link Author(s) R Gaina, C Nordmoen Abstract More info TBA Link

< Back ETHER: Aligning Emergent Communication for Hindsight Experience Replay Link Author(s) Kevin Denamganaï, Daniel Hernandez, Ozan Vardal, Sondess Missaoui, James Alfred Walker Abstract More info TBA Link

< Back Four dilemmas for video game effects scholars: How digital trace data can improve the way we study games Link Author(s) D Zendle, N Ballou, J Cutting, E Petrovskaya Abstract More info TBA Link

< Back Rinascimento: using event-value functions for playing Splendor Link Author(s) I Bravi, SM Lucas Abstract More info TBA Link

< Back Objective difficulty-skill balance impacts perceived balance but not behaviour: A test of flow and self-determination theory predictions Link Author(s) S Deterding, J Cutting Abstract More info TBA Link

< Back Dr Nuria Peña Pérez Queen Mary University of London iGGi Alum Nuria got her bachelor’s in biomedical engineering in Spain before moving to London. After studying an MSc in Neurotechnology and working in robotic neurorehabilitation at Imperial College London, she discovered the enormous potential of serious games in the field of human-robot interaction. She joined IGGI in 2018. Her PhD research involves studying human motor control and learning during bimanual tasks to investigate how the dynamics of the interaction can serve to develop better training systems. This is done through the development of interactive gaming environments that are compatible with rehabilitation robotic devices. The modelling of the recorded human neuromuscular data allows to explore how to better help patients to restore their motor function. Her work is a collaboration between the Advanced Robotics group at Queen Mary University of London and the Human Robotics group at Imperial College London. As part of her PhD she has worked for the company GripAble, developing games for the assessment and training of hand function (February 2020-August-2020). Email n.penaperez@qmul.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisor(s): Dr Ildar Farkhatdinov Featured Publication(s): Redundancy Resolution in Trimanual vs. Bimanual Tracking Tasks Dissociating haptic feedback from physical assistance does not improve motor performance Bimanual interaction in virtually and mechanically coupled tasks The impact of stiffness in bimanual versus dyadic interactions requiring force exchange How virtual and mechanical coupling impact bimanual tracking Lateralization of impedance control in dynamic versus static bimanual tasks Is a robot needed to modify human effort in bimanual tracking? Exploring user motor behaviour in bimanual interactive video games Quartz Crystal Resonator for Real-Time Characterization of Nanoscale Phenomena Relevant for Biomedical Applications Illuminating Game Space Using MAP-Elites for Assisting Video Game Design Themes Applied Games - Previous Next

< Back PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games Link Author(s) M Balla, GEM Long, D Jeurissen, J Goodman, RD Gaina, ... Abstract More info TBA Link

< Back TAG: Terraforming Mars Link Author(s) RD Gaina, J Goodman, D Perez-Liebana Abstract More info TBA Link

< Back Hexboard: A generic game framework for turn-based strategy games Link Author(s) J Walton-Rivers, PR Williams, R Bartle Abstract More info TBA Link