#deeprl — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #deeprl, aggregated by home.social.
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This #DeepRL paper from University of Alberta seems quite cool:
"Deep reinforcement learning without experience replay, target networks, or batch updates"
As the title says, they succeeded in training deep RL networks in streaming setting getting rid of replay buffers.
The main tricks for that to work seem to be signal normalization and bounding the step-size 🤯💻Code: http://github.com/mohmdelsayed/streaming-drl
📄Paper: https://openreview.net/pdf?id=yqQJGTDGXN -
This #DeepRL paper from University of Alberta seems quite cool:
"Deep reinforcement learning without experience replay, target networks, or batch updates"
As the title says, they succeeded in training deep RL networks in streaming setting getting rid of replay buffers.
The main tricks for that to work seem to be signal normalization and bounding the step-size 🤯💻Code: http://github.com/mohmdelsayed/streaming-drl
📄Paper: https://openreview.net/pdf?id=yqQJGTDGXN -
This #DeepRL paper from University of Alberta seems quite cool:
"Deep reinforcement learning without experience replay, target networks, or batch updates"
As the title says, they succeeded in training deep RL networks in streaming setting getting rid of replay buffers.
The main tricks for that to work seem to be signal normalization and bounding the step-size 🤯💻Code: http://github.com/mohmdelsayed/streaming-drl
📄Paper: https://openreview.net/pdf?id=yqQJGTDGXN -
This #DeepRL paper from University of Alberta seems quite cool:
"Deep reinforcement learning without experience replay, target networks, or batch updates"
As the title says, they succeeded in training deep RL networks in streaming setting getting rid of replay buffers.
The main tricks for that to work seem to be signal normalization and bounding the step-size 🤯💻Code: http://github.com/mohmdelsayed/streaming-drl
📄Paper: https://openreview.net/pdf?id=yqQJGTDGXN -
This #DeepRL paper from University of Alberta seems quite cool:
"Deep reinforcement learning without experience replay, target networks, or batch updates"
As the title says, they succeeded in training deep RL networks in streaming setting getting rid of replay buffers.
The main tricks for that to work seem to be signal normalization and bounding the step-size 🤯💻Code: http://github.com/mohmdelsayed/streaming-drl
📄Paper: https://openreview.net/pdf?id=yqQJGTDGXN -
So here's my introduction:
I am a Data Science MSc student at Nottingham Trent University. Mainly here to get updates on #ReinforcementLearning research and connect with people who has an interest in #RL.#reinforcementlearning #reinforcement_learning #RL #MARL #DeepRL
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This #FollowFriday I have the following recommendations:
For useful advice regarding Mastodon
@feditipsFor excellent #StreetPhotography
@omi_geekFor beautiful and unique looks into #Space
@kevinmgillFor discussion about the AI methods that can famously play Atari games:
#ReinforcementLearning
#DeepRL
#MARL
#RLOther hashtags worth following:
#ThrowbackThursday
#FollowFriday
#Caturday -
#AI #MSc_AI #Python #DeepLearning #AIEthics
#computationalcognition #creativeindustries #deeprl #ReinforcementLearningMSc in AI. Registration is open.
Requirements:
First (or, pending on CV, upper second class) BSc/BEng/BA in computer science, mathematics, physics, computer engineering, psychology or biology.
Competence in Python and MathematicsInterested?
For more information and to download a booklet, please visit https://cit-ai.net/CitAI-MSc.html -
#AI #MSc_AI #Python #DeepLearning #AIEthics
#computationalcognition #creativeindustries #deeprl #ReinforcementLearningMSc in AI. Registration is open.
Requirements:
First (or, pending on CV, upper second class) BSc/BEng/BA in computer science, mathematics, physics, computer engineering, psychology or biology.
Competence in Python and MathematicsInterested?
For more information and to download a booklet, please visit https://cit-ai.net/CitAI-MSc.html -
#AI #MSc_AI #Python #DeepLearning #AIEthics
#computationalcognition #creativeindustries #deeprl #ReinforcementLearningMSc in AI. Registration is open.
Requirements:
First (or, pending on CV, upper second class) BSc/BEng/BA in computer science, mathematics, physics, computer engineering, psychology or biology.
Competence in Python and MathematicsInterested?
For more information and to download a booklet, please visit https://cit-ai.net/CitAI-MSc.html -
#AI #MSc_AI #Python #DeepLearning #AIEthics
#computationalcognition #creativeindustries #deeprl #ReinforcementLearningMSc in AI. Registration is open.
Requirements:
First (or, pending on CV, upper second class) BSc/BEng/BA in computer science, mathematics, physics, computer engineering, psychology or biology.
Competence in Python and MathematicsInterested?
For more information and to download a booklet, please visit https://cit-ai.net/CitAI-MSc.html -
#AI #MSc_AI #Python #DeepLearning #AIEthics
#computationalcognition #creativeindustries #deeprl #ReinforcementLearningMSc in AI. Registration is open.
Requirements:
First (or, pending on CV, upper second class) BSc/BEng/BA in computer science, mathematics, physics, computer engineering, psychology or biology.
Competence in Python and MathematicsInterested?
For more information and to download a booklet, please visit https://cit-ai.net/CitAI-MSc.html -
Last year, a French #ai Athénan won 11/23 of the games at the 24th ICGA Computer Olympiad. Nice.
Even nicer: it ran on a single GPU, yet it challenged cutting-edge #deepRL-based AIs requiring hundreds of GPUs!
Here is the paper about its algorithm (a minimax): https://arxiv.org/pdf/2012.10700.pdf
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Last year, a French #ai Athénan won 11/23 of the games at the 24th ICGA Computer Olympiad. Nice.
Even nicer: it ran on a single GPU, yet it challenged cutting-edge #deepRL-based AIs requiring hundreds of GPUs!
Here is the paper about its algorithm (a minimax): https://arxiv.org/pdf/2012.10700.pdf
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Last year, a French #ai Athénan won 11/23 of the games at the 24th ICGA Computer Olympiad. Nice.
Even nicer: it ran on a single GPU, yet it challenged cutting-edge #deepRL-based AIs requiring hundreds of GPUs!
Here is the paper about its algorithm (a minimax): https://arxiv.org/pdf/2012.10700.pdf
-
Last year, a French #ai Athénan won 11/23 of the games at the 24th ICGA Computer Olympiad. Nice.
Even nicer: it ran on a single GPU, yet it challenged cutting-edge #deepRL-based AIs requiring hundreds of GPUs!
Here is the paper about its algorithm (a minimax): https://arxiv.org/pdf/2012.10700.pdf
-
Last year, a French #ai Athénan won 11/23 of the games at the 24th ICGA Computer Olympiad. Nice.
Even nicer: it ran on a single GPU, yet it challenged cutting-edge #deepRL-based AIs requiring hundreds of GPUs!
Here is the paper about its algorithm (a minimax): https://arxiv.org/pdf/2012.10700.pdf
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👉 New Preprint available #arXiv
http://arxiv.org/abs/2205.09738
AIGenC is a model for creative problem-solving in a deep reinforcement learning agent as a step forward to solving the problem of generalisation in #AI #deepRL -
👉 New Preprint available #arXiv
http://arxiv.org/abs/2205.09738
AIGenC is a model for creative problem-solving in a deep reinforcement learning agent as a step forward to solving the problem of generalisation in #AI #deepRL -
👉 New Preprint available #arXiv
http://arxiv.org/abs/2205.09738
AIGenC is a model for creative problem-solving in a deep reinforcement learning agent as a step forward to solving the problem of generalisation in #AI #deepRL -
👉 New Preprint available #arXiv
http://arxiv.org/abs/2205.09738
AIGenC is a model for creative problem-solving in a deep reinforcement learning agent as a step forward to solving the problem of generalisation in #AI #deepRL