Transferring expectations in model-based reinforcement learning

Transferring expectations in model-based reinforcement learning

We study how to automatically select and adapt multiple abstractions or representations of the world to support model-based reinforcement learning. We address the challenges of transfer learning in heterogeneous environments with varying tasks. We present an efficient, online framework that, through...

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Bibliographic Details
Main Authors: Nguyen, Trung Thanh, Silander, Tomi, Tze-Yun LEONG
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2012
Subjects:
Benchmark domains
Faster convergence
General approach
Heterogeneous environments
Mapping strategy
Model-based reinforcement learning
Potential impacts
Transfer learning
Numerical Analysis and Scientific Computing
Online Access:https://ink.library.smu.edu.sg/sis_research/3049
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Institution: Singapore Management University
Language: English
Description
Summary:We study how to automatically select and adapt multiple abstractions or representations of the world to support model-based reinforcement learning. We address the challenges of transfer learning in heterogeneous environments with varying tasks. We present an efficient, online framework that, through a sequence of tasks, learns a set of relevant representations to be used in future tasks. Without predefined mapping strategies, we introduce a general approach to support transfer learning across different state spaces. We demonstrate the potential impact of our system through improved jumpstart and faster convergence to near optimum policy in two benchmark domains.

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