Abstraction Networks
Lead Research Organisation:
Imperial College London
Department Name: Computing
Abstract
A Method For Knowledge Extraction Towards Lifelong
Reinforcement Learning
. Due to the requirement of autonomy, this project examines progress made in Lifelong
Reinforcement Learning (LRL) and identifies a promising development direction in the use
of technologies which incorporate a relational inductive bias over the knowledge representations. Finally, by integrating two prominent methods, namely Graph Networks and Latent
Feature Models, Abstraction Networks are proposed as a potential LRL framework, as they
assume a network of fundamental state-abstractions that have inherent synergy with the
knowledge manipulation requirements of lifelong learning.
Research area Reinforcement learning
Reinforcement Learning
. Due to the requirement of autonomy, this project examines progress made in Lifelong
Reinforcement Learning (LRL) and identifies a promising development direction in the use
of technologies which incorporate a relational inductive bias over the knowledge representations. Finally, by integrating two prominent methods, namely Graph Networks and Latent
Feature Models, Abstraction Networks are proposed as a potential LRL framework, as they
assume a network of fundamental state-abstractions that have inherent synergy with the
knowledge manipulation requirements of lifelong learning.
Research area Reinforcement learning
Organisations
People |
ORCID iD |
| Harald Stromfelt (Student) |
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/N509486/1 | 30/09/2016 | 30/03/2022 | |||
| 2286911 | Studentship | EP/N509486/1 | 31/03/2017 | 30/08/2021 | Harald Stromfelt |
| NE/W503198/1 | 31/03/2021 | 30/03/2022 | |||
| 2286911 | Studentship | NE/W503198/1 | 31/03/2017 | 30/08/2021 | Harald Stromfelt |