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Pytorch-based package for multi-agent reinforcement learning in an iterated prisonner dilemma setting

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th_rl

Pytorch-based package for multi-agent reinforcement learning in an iterated prisonner dilemma setting.

Installation

  • create python venv
python -m venv power_markets
power_markets\scripts\activate
  • install in local editable mode:
git clone https://github.com/nikitcha/th_rl
pip install -e th_rl
  • install as module from Github
pip install -U git+https://github.com/nikitcha/th_rl.git
  • if you want to install just dependencies:
pip install -r th_rl/requirements.txt

Usage - training

  • Create a set of training configs and store them somewhere, i.e. /some_path/configs
    • Configs should follow the structured laid out in 'example_config.json'
  • Run training with the follwing command:
    • This would run each config 20 times
    • Result will be stored under /some_path/runs
python main.py --cdir=/some_path/configs --runs=20  

Or if installed:

python -m th_rl.main.py --cdir=/some_path/configs --runs=20  

Usage - plot results

  • To plot a single trajectory:
python utils.py --dir=/some_path/runs/qtable_001/1 --fun=plot_experiment
  • To plot the mean of all [20] runs:
python utils.py --dir=/some_path/runs/qtable_001 --fun=plot_mean_result

Or if installed:

python -m th_rl.utils --dir=/some_path/runs/qtable_001 --fun=plot_mean_result

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Pytorch-based package for multi-agent reinforcement learning in an iterated prisonner dilemma setting

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