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Code used for "Introducing neuromodulation in deep neural networks to learn adaptive behaviours" paper.

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NMN

Usage

Go into the top level directory, that is "cd .../nmd_net/". From that directory, launch the "meta_rl_launcher" as:

./ReinforcementLearning/Training/meta_rl_launcher.py
  1. -b option allows to chose de benchmark
  2. -g options allows to chose wheter to run the program with or without GPU. GPU is active with the -g option.
  3. -e sets the episodes budget
  4. -t sets the type of architecture to use. "recurrent" for classic rnn and "nmdnet" for NMNs.
  5. -tb, -bs and -o options allow to chose a number of tests to launch and the starting test number. When running the program, it will start by running test number "bs * tb + o" and once done will run test number "bs * tb + o + 1" and so on until reaching test "bs * (tb + 1)"
  6. -l allows to chose whether to start from scratch or load a previous test (which must exist). -l checkpoint will load the latest network trained for that particular test number.

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Code used for "Introducing neuromodulation in deep neural networks to learn adaptive behaviours" paper.

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  • Python 100.0%