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All experiment notebooks are in experiments directory. To run them ou might have to move them to root directory first and then run them.
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Also some of them were not run after the recent changes. (probably should not include this in final version).
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Proposal is in proposal directory.
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Final Report is in report directory.
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tranfer knowledge from one player to other.
- Command to generate bk2 given a trained model:
python main.py --command record --bias False --capture_movement True --record_path /tmp/record/ --render True --model_path experiments/final_models/unbiased_capture_movement/A2C_GUILE.zip --state guile.state
- Command to tune model using optuna, for all states:
It uses Bayesian Hyper parameter tuning from BoTorch sampler.
python main.py --command tune --n_jobs 4 --bias False --capture_movement True
for one state:
python main.py --command tune --bias True --capture_movement True --n_jobs 1 --state chunli.state --model_path experiments/final_models/bias_capture_movement/A2C_CHUNLI.zip --trials 4
Currently range of hyperparameters to train are hardcoded in tuner class. Those are
{
'gamma': trial.suggest_loguniform('gamma', 0.8, 0.85),
'learning_rate': trial.suggest_loguniform('learning_rate', 1e-4, 4e-4),
'gae_lambda': trial.suggest_uniform('gae_lambda', 0.8, 0.9)
}
- Command to train model, for all states:
python main.py --command train --bias True --capture_movement False --n_jobs 1
for one state
python main.py --command train --bias True --capture_movement False --n_jobs 1 --state guile.state
Currently all model params are hardcoded in main.py itself for training and ease of training.
- Command to fine tune a trained model for a state:
python main.py --command fine_tune --model_path experiments/final_models/biased_capture_movement/A2C_CHUNLI.zip --state chunli.state --bias True --capture_movement True
- Command to add a new environment:
python -m retro.import .
- Command to generate mp4 from bk2
python -m retro.scripts.playback_movie *bk2
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