UNIVERSITY OF HERTFORDSHIRE
             COMPUTER SCIENCE RESEARCH COLLOQUIUM

                            presents

           "Bayesian Machine Learning for Controlling
                       Autonomous Systems"

                      Dr. Marc Deisenroth
            (Dept. Computing, Imperial College London)

                  19 February 2014 (Wednesday)
                     *** 11 am -12 noon ***

                  Hatfield, College Lane Campus
                       Seminar Room D120 

                  Everyone is Welcome to Attend

                  Refreshments will be available

Abstract:

Autonomous learning has been a promising direction in control and
robotics for more than a decade since learning models and controllers
from data allows us to reduce the amount of engineering knowledge that is
otherwise required.  Due to their flexibility, autonomous reinforcement
learning (RL) approaches typically require many interactions with the
system to learn controllers. However, in real systems, such as robots,
many interactions can be impractical and time consuming. To address this
problem, current learning approaches typically require task-specific
knowledge in form of expert demonstrations, pre-shaped policies, or
specific knowledge about the underlying dynamics.

In the first part of the talk, we follow a different approach and speed
up learning by efficiently extracting information from sparse data. In
particular, we learn a probabilistic, non-parametric Gaussian process
dynamics model. By explicitly incorporating model uncertainty into
long-term planning and controller learning our approach reduces the
effects of model errors, a key problem in model-based learning. Compared
to state-of-the art RL our model-based policy search method achieves
an unprecedented speed of learning. We demonstrate its applicability to
autonomous learning in real robot and control tasks.

In the second part of my talk, we will discuss an alternative method for
learning controllers based on Bayesian Optimization, where it is no longer
possible to learn models of the underlying dynamics. We successfully
applied Bayesian optimization to learning controller parameters for
a bipedal robot, where modeling the dynamics is very difficult due to
ground contacts. Using Bayesian optimization, we sidestep this modeling
issue and directly optimize the controller parameters without the need
of modeling the robot's dynamics.

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Hertfordshire Computer Science Research Colloquium
http://cs-colloq.stca.herts.ac.uk