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