Noisy Networks For Exploration Article Swipe
Meire Fortunato
,
Mohammad Gheshlaghi Azar
,
Bilal Piot
,
Jacob Menick
,
Ian Osband
,
Alexander Graves
,
Vlad Mnih
,
Rémi Munos
,
Demis Hassabis
,
Olivier Pietquin
,
Charles Blundell
,
Shane Legg
·
YOU?
·
· 2018
· Open Access
·
YOU?
·
· 2018
· Open Access
·
We introduce NoisyNet, a deep reinforcement learning agent with parametric noise added to its weights, and show that the induced stochasticity of the agent's policy can be used to aid efficient exploration. The parameters of the noise are learned with gradient descent along with the remaining network weights. NoisyNet is straightforward to implement and adds little computational overhead. We find that replacing the conventional exploration heuristics for A3C, DQN and dueling agents (entropy reward and $\epsilon$-greedy respectively) with NoisyNet yields substantially higher scores for a wide range of Atari games, in some cases advancing the agent from sub to super-human performance.
Related Topics
Concepts
Heuristics
Reinforcement learning
Computer science
Entropy (arrow of time)
Range (aeronautics)
Artificial intelligence
Mathematical optimization
Noise (video)
Gradient descent
Overhead (engineering)
Parametric statistics
Artificial neural network
Mathematics
Engineering
Physics
Operating system
Quantum mechanics
Statistics
Aerospace engineering
Image (mathematics)
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://arxiv.org/pdf/1706.10295.pdf
- OA Status
- green
- Cited By
- 272
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2964174623
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W2964174623Canonical identifier for this work in OpenAlex
- Title
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Noisy Networks For ExplorationWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-02-15Full publication date if available
- Authors
-
Meire Fortunato, Mohammad Gheshlaghi Azar, Bilal Piot, Jacob Menick, Ian Osband, Alexander Graves, Vlad Mnih, Rémi Munos, Demis Hassabis, Olivier Pietquin, Charles Blundell, Shane LeggList of authors in order
- Landing page
-
https://arxiv.org/pdf/1706.10295.pdfPublisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/1706.10295.pdfDirect OA link when available
- Concepts
-
Heuristics, Reinforcement learning, Computer science, Entropy (arrow of time), Range (aeronautics), Artificial intelligence, Mathematical optimization, Noise (video), Gradient descent, Overhead (engineering), Parametric statistics, Artificial neural network, Mathematics, Engineering, Physics, Operating system, Quantum mechanics, Statistics, Aerospace engineering, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
272Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2024: 3, 2023: 4, 2022: 21, 2021: 80Per-year citation counts (last 5 years)
- Related works (count)
-
20Other works algorithmically related by OpenAlex
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