![[Search]](/static/images/search.gif){kind=small}
![[A-Z Index]](/static/images/az.gif){kind=small}
![[Contact]](/static/images/contact.gif){kind=small}
![[University of Cambridge]](/static/images/identifier2.gif){kind=small}
![[Talks.cam]](/static/images/talkslogosmall.gif)
Thursday 06 November 2014, 15:00-16:30
State Space Abstraction for Reinforcement Learning
- 👤 Speaker: Rowan McAllister; Thang Bui
- 📅 Date & Time: Thursday 06 November 2014, 15:00 - 16:30
- 📍 Venue: Engineering Department, CBL Room 438
Abstract
Reinforcement learning (RL) is a method of solving sequential decision tasks in Markov decision process frameworks with unknown parameters. Unfortunately the computational complexity of RL hinders application to many real world problems. Function approximation techniques are a common method to compute approximate solutions fast. An alternative technique is state space abstraction, discarding irrelevant state information. Abstracted state spaces speed up learning exponentially w.r.t. state dimensionality. An extra benefit is discovery of powerful generalisations in the original state space. This talk will provide a review of state space abstraction. We introduce different types of abstractions and their consequences to the solution accuracy. We will also discuss predictive state representations: a compact way to model dynamical systems using predictions of observable quantities.
Series This talk is part of the Machine Learning Reading Group @ CUED series.
Included in Lists
- All Talks (aka the CURE list)
- bld31
- Cambridge Centre for Data-Driven Discovery (C2D3)
- Cambridge Forum of Science and Humanities
- Cambridge Language Sciences
- Cambridge talks
- Cambridge University Engineering Department Talks
- Centre for Smart Infrastructure & Construction
- Chris Davis' list
- Computational Continuum Mechanics Group Seminars
- custom
- Engineering Department, CBL Room 438
- Featured lists
- Guy Emerson's list
- Hanchen DaDaDash
- Inference Group Journal Clubs
- Inference Group Summary
- Information Engineering Division seminar list
- Interested Talks
- Machine Learning Reading Group
- Machine Learning Reading Group @ CUED
- Machine Learning Summary
- ML
- ndk22's list
- ob366-ai4er
- Quantum Matter Journal Club
- Required lists for MLG
- rp587
- School of Technology
- Simon Baker's List
- TQS Journal Clubs
- Trust & Technology Initiative - interesting events
- yk373's list
- yk449
Note: Ex-directory lists are not shown.