Alexander Telfar

Masters Student School of Engineering and Computer Science

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Thesis Info

Research Interests: Learning, controls, computation
Thesis Title: Model-based reinforcement learning
Supervisor: Will Browne

 

Research Interests

I love the idea of understanding the "limits of intelligence". Our limits, the limits of computers and the limits of the universe. What is computationally/physically possible given a set of resources?

Some problems are easy to solve, yet others are just fundamentally hard (like planning a road trip - travelling salesman problem, see P vs NP). I am curious about what computational (or cognitive, if you like) structures/functions are required to efficiently solve everyday problems and to understand the world around us.

If an agent is given access to a memory buffer, can certain problems be solved more efficiently than others? An answer to this would hint at the advantage an animal would have in evolving (working) memory.