Ph

Colloquium: Exploration in Reinforcement Learning

date
15.10.2018 
time
04:30 PM - 05:30 PM 
speaker
Prof. Ron Meir 
affiliation
Technion, Haifa, Israel 
part of series
MPI-PKS Colloquium 
language
en 
main topic
Physics: Theoretical Physics
abstract

In Reinforcement Learning an agent learns how to act in an environment in order to achieve desired goals. In general, the agent only has access to partial information from the world, and has to learn how to act appropriately in face of this uncertainty. One of the earliest results in the theory of Optimal Control (Feldbaum 1965) demonstrates that in order to act appropriately, the agent must also probe the environment in order to gain information and reduce uncertainty. In other words, exploring the environment is an essential component of learning to act. However, very few provably effective procedures were proposed in the control literature to solve this problem. In recent years, several elegant solutions to the problem of exploration have been proposed in the Machine Learning literature for the case of finite state and action spaces (which was of little concern to the control theorists). However, the problem of effective exploration in continuous spaces is still very much open, and of increasing importance in many applications. We survey some recent approaches to exploration in continuous domains, in both model based and model free settings, and suggest open problems for future research.

 

Last update: 15.10.2018 00:09.

venue 

Max-Planck-Institut für Physik komplexer Systeme (Seminarroom 1+2) 
Nöthnitzer Straße 38
01187 Dresden
telefon
+ 49 (0)351 871 0 
e-mail
Max-Planck-Institut für Physik komplexer Systeme 
homepage
http://www.mpipks-dresden.mpg.de 

organizer 

Max-Planck-Institut für Physik komplexer Systeme (MPI-PKS)
Nöthnitzer Straße 38
01187 Dresden
telefon
+ 49 (0)351 871 0 
e-mail
Max-Planck-Institut für Physik komplexer Systeme (MPI-PKS) 
homepage
http://www.mpipks-dresden.mpg.de 
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