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General discounting versus average reward

Date

Authors

Hutter, Marcus

Publisher

Springer

Abstract

Consider an agent interacting with an environment in cycles. In every interaction cycle the agent is rewarded for its performance. We compare the average reward U from cycle 1 to m (average value) with the future discounted reward V from cycle k to ∞ (d

Keywords

Keywords: Artificial intelligence; Asymptotic stability; Computer simulation; Interactive computer systems; Arbitrary reward sequences; Interaction cycle; Intelligent agents

URI

http://hdl.handle.net/1885/28236

Collections

ANU Research Publications

Source

Proceedings of International Conference on Algorithmic Learning Theory (ALT 2006)

Type

Conference paper

Restricted until

2037-12-31

Downloads

File

Description

01_Hutter_General_discounting_versus_2006.pdf (130.88 KB)

02_Hutter_General_discounting_versus_2006.pdf (160.2 KB)

03_Hutter_General_discounting_versus_2006.pdf (399.42 KB)