My focus is on developing sample-efficient algorithms for exploration, coordination, and communication in multi-agent reinforcement learning using insights from game theory.
I believe bridging the gap between practical deep learning and theoretical models of stochastic optimisation is essential for scaling RL in real-world MARL settings.
I build algorithms which exhibit high performance in high-dimensional environments while providing mathematical insights using probability theory, linear algebra, calculus, & functional analysis.
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