Welcome to the repository for the RLC 2024 paper titled "Boosting Soft Q-Learning by Bounding" by Jacob Adamczyk, Volodymyr Makarenko, Stas Tiomkin, and Rahul Kulkarni Here you can reproduce the paper's experiments, and try bounded soft Q-learning on your own environments.

We use Gymnasium for RL environments and wandb for logging / hyperparameter tuning.

Setup

To prepare an environment with conda:

1. setup a conda env conda create --name qbounds python=3.10
2. activate the conda env conda activate qbounds
3. python requirements: pip install -r requirements.txt

FA experiments

reproduce the finetuned results

1. Run the best hparams for an environment
   python experiments.py --env_id=CartPole-v1

2. plot the results for the spcific environment
   python tb_plotter.py --env_id=CartPole-v1

re-run the hparam sweep

1. Run the hparam sweep for an environment
   python experiments.py --env_id=CartPole-v1 --do_sweep=True
2. extract the best hparams logged with wandb
   python wandb_best_hparams.py --env_id=CartPole-v1 --entity=your_wandb_entity

Tabular experiments

reproduce the figures

1. Run the lr sweep for each of the methods of interest. It will use 30 mazes contained in the mazes directory. python tabular_experiments/tabular_lr_sensitivity.py