README.md

BiasNet: Learning Relational Bias from Differential Scenes for Deep Reinforcement Learning

• All experiment notebooks are in experiments directory. To run them ou might have to move them to root directory first and then run them.

• Also some of them were not run after the recent changes. (probably should not include this in final version).

• Proposal is in proposal directory.

• Final Report is in report directory.

• tranfer knowledge from one player to other.

Instructions to run experiments

• 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

Few useful retro commands

• Command to add a new environment:

python -m retro.import .

• Command to generate mp4 from bk2

python -m retro.scripts.playback_movie *bk2

Results and experiments are in experiments directory.

Videos for "CHUNLI vs. AGENT" are here.