Pytorch implementation of Deep Q-Networks (with different operators) for Flappy Bird environment. Deep Q-Learning has been shown to suffer from an overestimation problem due to the use of the max operator (due to Jensen's inequality and convexity of the max). Multiple replacements for the max operator are tested in the Flappy Bird environment.
Links to the original papers are embedded below:
Mellowmax:
Asadi, K., & Littman, M. L. (2017, June 14). An alternative softmax operator for reinforcement learning. arXiv.org. Retrieved September 13, 2022, from https://arxiv.org/abs/1612.05628
Adaptive Mellowmax:
“Adaptive temperature tuning for mellowmax in deep ... - brown university.” [Online]. Available: https://cs.brown.edu/people/gdk/pubs/tuning_mellowmax_drlw.pdf. [Accessed: 14-Sep-2022].
Soft Mellowmax:
Y. Gan, Z. Zhang, and X. Tan, “Stabilizing Q learning via soft Mellowmax operator,” arXiv.org, 18-Dec-2020. [Online]. Available: https://arxiv.org/abs/2012.09456. [Accessed: 13-Sep-2022].
Install requirements:
pip install -r requirements.txt
First, determine the algorithm you want to run. You can modify the parameters of the code in the corresponding config file in the configs folder. To help you better understand the input parameters, we list them below:
model_save_path: folder to save policy model
load_path: load a model's weight from this path
results_save_path: folder to save the quantitative data that is recorded every iteration
game_run_path: folder to save quantitative data saved during an episode
mode: train or test model
save_every: save model, data every X iteration
temperature: temperature of an operator
After setting the parameters to your preference, run
python deepmellow_dqn.py --config configs/dm_param.txt
for the deepmellow operator. Change the .py and .txt files accordingly if you want to use another operator.
This code borrows the Flappy Bird interface from:
https://github.com/nevenp/dqn_flappy_bird
We thank the author for the contribution to this project.