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random-network-distillation

Code for the paper "Exploration by Random Network Distillation"

Model DevelopmentReinforcement LearningPython
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创建于 2018-10-16 · 更新于 2026-10-10 · 今日第 12573 名
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yburdacbernerchristopherhesse

openai/random-network-distillation 排名第 12,573，有 930 个星标。

同类别比较

openai/random-network-distillation 排名第 12,573，此处有 8 个同类条目；microsoft/SoftTeacher 排名第 12,571。

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关键参数与规格
关键参数与规格
参数值
语言Python
类别Model Development
子类别Reinforcement Learning
关键词RL
星标数930
分叉数166
贡献者4
创建日期2018-10-16
更新日期2026-10-10
1 天星标数0
7 天星标数1
排名12,573
上次排名12,492
排名变化-81
7 天星标排名10,425
上次 7 天星标排名10,478
7 天星标排名变化53
总星标排名11,777
上次总星标排名11,763
总星标排名变化-14
国家USA
README 摘录

**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).

常见问题

记录了多少星标和分叉？

openai/random-network-distillation 有 930 个星标和 166 个分叉。

仓库排名是多少？

openai/random-network-distillation 排名第 12,573。

记录的语言和类别是什么？

openai/random-network-distillation 有 930 个星标，语言为 Python，类别为 Model Development。

哪个仓库排名最接近？

openai/random-network-distillation 与 microsoft/SoftTeacher 比较，后者排名第 12,571，有 930 个星标。

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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