Greener deep reinforcement learning: analysis of energy and carbon efficiency across Atari benchmarks

Jason Gardner; Ayan Dutta; Swapnoneel Roy; O. Patrick Kreidl; Ladislau Bölöni

doi:10.1007/s10462-026-11520-x

Greener deep reinforcement learning: analysis of energy and carbon efficiency across Atari benchmarks

Jason Gardner
, Ayan Dutta
, Swapnoneel Roy
, O. Patrick Kreidl
, Ladislau Bölöni

School of Computing

University of Central Florida

Research output: Contribution to journal › Article › peer-review

Original language	English
Article number	148
Journal	Artificial Intelligence Review
Volume	59
Issue number	6
DOIs	https://doi.org/10.1007/s10462-026-11520-x
State	Published - Jun 2026

ASJC Scopus Subject Areas

Language and Linguistics
Linguistics and Language
Artificial Intelligence

Keywords

Deep reinforcement learning
Electricity cost
Energy efficiency
Greenhouse gas emissions

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10.1007/s10462-026-11520-x

Cite this

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keywords = "Deep reinforcement learning, Electricity cost, Energy efficiency, Greenhouse gas emissions",

author = "Jason Gardner and Ayan Dutta and Swapnoneel Roy and \{Patrick Kreidl\}, O. and Ladislau B{\"o}l{\"o}ni",

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AU - Gardner, Jason

AU - Dutta, Ayan

AU - Roy, Swapnoneel

AU - Patrick Kreidl, O.

AU - Bölöni, Ladislau

N1 - Publisher Copyright: © The Author(s) 2026.

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Y1 - 2026/6

KW - Deep reinforcement learning

KW - Electricity cost

KW - Energy efficiency

KW - Greenhouse gas emissions

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Greener deep reinforcement learning: analysis of energy and carbon efficiency across Atari benchmarks

ASJC Scopus Subject Areas

Keywords

Access to Document

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