Code for the paper Monte Carlo Tree Search for Asymmetric Trees by Thomas M. Moerland, Joost Broekens, Aske Plaat and Catholijn M. Jonker.
- Install recent versions of:
- Python 3
- Tensorflow
- Numpy
- Matplotlib
- Clone this repository:
git clone https://github.com/tmoer/mcts-t.git
You can run a new experiment from the agent.py function. Hyperparameters can be parsed through the --hp option. Default hyperparameters are listed in mcts-t+/hps.py. For example, to start a default experiment on CartPole-v0:
cd mcts-t+
python3 agent.py --hp game=CartPole-v0
The results of the paper can be reproduced by:
cd mcts-t+
bash jobs/paper_jobs.sh
This automatically loop over the necessary hyperparameters. Running it will take quite long on a regular laptop though. You can submitted the runs to a SLURM cluster via
bash jobs/paper_jobs_slurm.sh
Subsequently, you can visualize the output with
cd mcts-t+
python3 visualize.py --home --plot_type mean --game your_game
for some your_game of your choice.
@proceedings{moerland2018monte,
author = "Moerland, Thomas M and Broekens, Joost and Plaat, Aske and Jonker, Catholijn M",
journal = "arXiv preprint arXiv:1805.09218",
title = "{Monte Carlo Tree Search for Asymmetric Trees}",
year = "2018"
}