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How many random seeds are needed to compare #DeepRL algorithms?
Our new tutorial to address this key issue of #reproducibility in #reinforcementlearning
PDF: arxiv.org/pdf/1806.08295…
Code: github.com/flowersteam/rl…
Blog: openlab-flowers.inria.fr/t/how-many-ran…
#machinelearning #neuralnetworks
Our new tutorial to address this key issue of #reproducibility in #reinforcementlearning
PDF: arxiv.org/pdf/1806.08295…
Code: github.com/flowersteam/rl…
Blog: openlab-flowers.inria.fr/t/how-many-ran…
#machinelearning #neuralnetworks
Algo1 and Algo2 are two famous #DeepRL algorithms, here tested
on the Half-Cheetah #opengym benchmark.
Many papers in the litterature compare using 4-5 random seeds,
like on this graph which suggests that Algo1 is best.
Is this really the case?
on the Half-Cheetah #opengym benchmark.
Many papers in the litterature compare using 4-5 random seeds,
like on this graph which suggests that Algo1 is best.
Is this really the case?
However, more robust statistical tests show there are no differences.
For a very good reason: Algo1 and Algo2 are both the same @openAI baseline
implementation of DDPG, same parameters!
This is what is called a "Type I error" in statistics.
For a very good reason: Algo1 and Algo2 are both the same @openAI baseline
implementation of DDPG, same parameters!
This is what is called a "Type I error" in statistics.
