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random-network-distillation

Code for the paper "Exploration by Random Network Distillation".

Model DevelopmentReinforcement LearningPython
Open on GitHub
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930
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166
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+1
This week
4
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Created 2018-10-16 · Updated 2026-10-10 · #12573 today
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yburdacbernerchristopherhesse

openai/random-network-distillation is ranked 12,573 with 930 stars.

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openai/random-network-distillation is ranked 12,573 among these 8 peers; microsoft/SoftTeacher is ranked 12,571.

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Key parameters and specifications
Key parameters and specifications
ParameterValue
LanguagePython
CategoryModel Development
SubcategoryReinforcement Learning
KeywordsRL
Stars930
Forks166
Contributors4
Created date2018-10-16
Updated date2026-10-10
Stars in 1 day0
Stars in 7 days1
Rank12,573
Previous rank12,492
Rank change-81
7-day stars rank10,425
Previous 7-day stars rank10,478
7-day stars rank change53
Total stars rank11,777
Previous total stars rank11,763
Total stars rank change-14
CountryUSA
README summary

**Status:** Archive (code is provided as-is, no updates expected) Exploration by Random Network Distillation ## Yuri Burda*, Harri Edwards*, Amos Storkey, Oleg Klimov *equal contribution OpenAI University of Edinburgh Installation and Usage The following command should train an RND agent on Montezuma's Revenge To use more than one gpu/machine, use MPI (e. should use 1024 parallel environments to collect experience on an 8 gpu machine).

Frequently asked questions

How many stars and forks are recorded?

openai/random-network-distillation has 930 stars and 166 forks.

What is the repository rank?

openai/random-network-distillation is ranked 12,573.

What language and category are recorded?

openai/random-network-distillation has 930 stars, uses Python and belongs to Model Development.

Which repository is closest in rank?

openai/random-network-distillation is compared with microsoft/SoftTeacher, ranked 12,571 with 930 stars.

README

Status: Archive (code is provided as-is, no updates expected)

Exploration by Random Network Distillation

Yuri Burda*, Harri Edwards*, Amos Storkey, Oleg Klimov

*equal contribution

OpenAI

University of Edinburgh

Installation and Usage

The following command should train an RND agent on Montezuma's Revenge

python run_atari.py --gamma_ext 0.999

To use more than one gpu/machine, use MPI (e.g. mpiexec -n 8 python run_atari.py --num_env 128 --gamma_ext 0.999 should use 1024 parallel environments to collect experience on an 8 gpu machine).

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