Geraud Tasse is a lecturer and reinforcement learning researcher with nine years of experience bridging mathematics, physics and computer science in both teaching and research. Based at the University of the Witwatersrand, he designs and delivers honours and postgraduate courses in Reinforcement Learning and Computational Intelligence while supervising MSc students and building a course on the mathematical and statistical foundations of data science. His PhD work focuses on compositional generalisation in hierarchical reinforcement learning, complementing hands-on research roles and earlier applied software and web development experience. Known for a strong theoretical grounding, he combines deep mathematical insight with practical optimisation techniques such as evolutionary algorithms and particle swarm optimisation.
9 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy - PhD, Reinforcement Learning, Doctor of Philosophy - PhD, Reinforcement Learning at University of the Witwatersrand
Machine learning, Algorithms, Neural networks, Neurosciene, Game theory, Machine learning, Algorithms, Neural networks, Neurosciene, Game theory at Coursera
Web developement, Game Development, Computer graphics, Web developement, Game Development, Computer graphics at Udacity
Honours Degree, Mathematics and Computer Science, Honours Degree, Mathematics and Computer Science at Rhodes University
Code for the paper "A Boolean Task Algebra For Reinforcement Learning"
Contributions:12 commits, 3 pushes in 5 months
reinforcement-learningalgebrareinforcementboolean
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Geraud Tasse - Lecturer at University of the Witwatersrand