Implementation of ReBeL, an algorithm that generalizes the paradigm of self-play reinforcement learning and search to imperfect-information games. This repository contains implementation only for Liar's Dice game.

The recommended way to install ReBeL is via conda env.

First, install dependencies:

pip install -r requirements.txt
conda install cmake
git submodule update --init

Then, compile the C part:

make

Use the following command to train a value net:

python run.py --adhoc --cfg conf/c02_selfplay/liars_sp.yaml \
    env.num_dice=1 \
    env.num_faces=4 \
    env.subgame_params.use_cfr=true \
    selfplay.cpu_gen_threads=60

Check the config conf/c02_selfplay/liars_sp.yaml for all possible parameters. If use use Slurm to manage the cluster, add launcher=slurm to run the job on the cluster.

The trainer saves checkpoints every 10 epochs as state dictionaries and as TorchScript modules. You can use the latter to compute exploitability of strategy produced with such a model using the following command:

build/recursive_eval \
    --net path/to/model.torchscript \
    --mdp_depth 2 \
    --num_faces 4 \
    --num_dice 1 \
    --subgame_iters 1024 \
    --num_repeats 4097 \
    --num_threads 10 \
    --cfr

Setting --num_repeats to a positive value enables evaluation of a sampled policy, i.e., when we use a randomly selected iteration of the underlying subgame algorithm for the subgame. Computing the exact full policy produced by such a process is intractable. Therefore, we average num_repeats such policies to get an upper bound for the exploitability.

The script reports exploitability for both full tree solving and recursive solving.

We release checkpoints of value function for games 1x4f, 1x5f, 1x6f, and 2x3f. We report the average exploitability of these checkpoints in the paper. Use eval_all.py script to download and evaluate all the models.

The training loop is implemented in Python and located in cfvpy/selfplay.py. The actual data generation part happens in C and could be found in csrc/liarc_dice.

Rebel is released under the Apache license. See LICENSE for additional details.

@article{brown2020combining,
    title={Combining Deep Reinforcement Learning and Search for Imperfect-Information Games},
    author={Noam Brown and Anton Bakhtin and Adam Lerer and Qucheng Gong},
    year={2020},
    journal={arXiv:2007.13544}
}