Speaker |
Chris Watkins |
|---|---|
Affiliation |
Royal Holloway University of London |
Date |
Friday, 16 May 2014 |
Time |
13:00-14:00 |
Location |
Zoom |
Link |
MPEB 1.03 |
Event series |
DeepMind/ELLIS CSML Seminar Series |
Abstract |
Abstract: Classic models from population genetics can be adapted to give evolutionary algorithms that are MCMC methods of a type widely used in Bayesian inference. Breeding is modelled using a generalisation of the Moran process; selection is modelled as a Metropolis acceptance. The result is a family of finite-population algorithms, for which the Markov chain of populations satisfies detailed balance, and the stationary distribution factorises exactly into a simple form. These algorithms are closely analogous to Gibbs-within-Metropolis algorithms for Bayesian inference. We will consider a range of such probability models for both sexual and asexual evolution. Some basic information-theoretic differences between sexual and asexual reproduction become obvious using this approach. Initial results on optimising movement of a robot arm will be described. From the point of view of evolutionary computation, we propose new type of 'genetic algorithm', with known good statistical properties. Evolution is perhaps the natural world's number one learning algorithm: this talk presents computational models of evolution that are examples of a standard MCMC approach widely used in machine learning. Bio: Dr. Chris Watkins is a reader in Computer Science at Royal Holloway University of London. His research interests have been in computational finance, kernel methods, evolutionary theory, and behavioural learning. He obtained his PhD from Cambridge University, and in his thesis he proposed that behavioural learning could be considered as using experience for incremental policy optimisation in a Markov decision process (MDP), and he introduced the Q-learning algorithm: this work was influential and became one of the standard models of reinforcement learning. |
Biography |