HoK Offline

Here is the open-access code for NeurIPS2023 Datasets and Benchmarks accepted paper "Hokoff: Real Game Dataset from Honor of Kings and its Offline Reinforcement Learning Benchmarks". Datasets can be downloaded in our website. Based on this code, you can realize the comprehensive process for offline RL and offline MARL based on Honor of Kings Arena (HoK1v1) and Honor of Kings Multi-Agent Arena (HoK3v3). The framework is demonstrated as follow:

Installation

HoK Env

Our framework is based on Honor of Kings environment, please install the hok environment and the gamecore first. [Please install the environment with the code tagged as "v2.0.0"]

For running on Linux, following the introductions in hok_env/docs/run_windows_gamecore_on_linux.md, you can make a gamecore docker image. Then by running the following example commands, you can start a gamecore server container for hok1v1 or hok3v3.

# hok1v1 gamecore server container
docker run --name gamecore1v1 -d --network host --restart=always -e SIMULATOR_USE_WINE=1 -e GAMECORE_SERVER_BIND_ADDR=":23333" gamecore:latest bash -c "bash /rl_framework/remote-gc-server/run_and_monitor_gamecore_server.sh"

# hok3v3 gamecore server container
docker run --name gamecore3v3 -d --network host --restart=always -e SIMULATOR_USE_WINE=1 -e GAMECORE_SERVER_BIND_ADDR=":23432" gamecore:latest bash -c "bash /rl_framework/remote-gc-server/run_and_monitor_gamecore_server.sh"

Multi-Level Models

The Multi-Level Models we presented in the paper can be download from the following link: for HoK1v1 and for HoK3v3. Please download these models and unzip them into folder hok1v1 and folder hok3v3 respectively. Here is the code example:

# download multi-level models for HoK Arena
cd hok1v1
wget https://kaiwu-assets-1258344700.cos.ap-shanghai.myqcloud.com/paper/hok-offline/1v1/1v1baselines.zip
unzip 1v1baselines.zip
rm 1v1baselines.zip
mv 1v1baselines baselines

# download multi-level models for HoK MA2
cd hok3v3
wget https://kaiwu-assets-1258344700.cos.ap-shanghai.myqcloud.com/paper/hok-offline/3v3/3v3baselines.zip
unzip 3v3baselines.zip
rm 3v3baselines.zip
mv 3v3baselines baselines

Requirements

The python version we use is Python 3.7.13

Please install other requirements by:

pip install -r requirements.txt

Sample

cd hok3v3
sh offline_sample/scripts/start_sample.sh <levels_str> <eval_num> <cpu_num> <datasets_repo_name>`<backend>` <dataset_name>
#e.g.
bash offline_sample/scripts/start_sample.sh 1,1 20 50 3v3version1 tensorflow norm_medium

Please refer to 'start_sample.sh' and 'sample.sh' for details.

levels_str: the levels of pre-trained models used for sampling, e.g. '1,1' for norm_medium, '2,1' for norm_expert and 'medium' for gain_gold_medium

eval_num & cpu_num: the sample module will start cpu_num parallel processes and each process will sample eval_num episodes, i.e. the total num of sampled trajectories is cpu_num*eval_num.

dataset_repo_name: the sampled datasets will be stored in either "hok1v1/datasets" or "hok3v3/datasets" directory, while dataset_repo_name represents the name of the datasets repository subdirectory within the "datasets" folder.

backend: refers to the selection of either "pytorch" or "tensorflow" models for the sampling process.

dataset_name: refers to the name of the dataset and the folder name within the "/datasets/dataset_repo_name" directory in which the sampled hdf5 files are stored.

Notations:

1. The sampled data is saved into hdf5 files. Assuming that levels_str='model1,model2', the samples collected in the nth parallel sampling process will be stored in n_0.hdf5 and n_1.hdf5, where n_0.hdf5 contains the data for model1 and n_1.hdf5 contains the data for model2. Therefore, further processing is necessary in order to build a standardized offline reinforcement learning dataset.
2. During parallel sampling, some of the sampling processes may fail for various gamecore reasons. Apart from checking the log files in "offline_sample/logs", you can use the "tools/remove_and_rename.py" tool to verify if the collected data files are readable and complete.

Train

cd hok3v3
python offline_train/train.py --root_path=offline_logs --replay_dir=datasets --dataset_name=norm_medium --run_prefix=run_indbc_0

Notations:

1. The models and train_logs and tensorboard files will be saved into the directory 'hokoff/hok3v3/offline_logs/run_indbc_0' which follows the format "<root_path>/<run_prefix>"
2. The format for the run_prefix is 'run_<algorithm_name>_<experiment_id>'. Please adhere to this format unless you are modifying the code for your own purposes.

Evaluate

cd hok3v3
python offline_eval/evaluation.py --root_path=offline_logs --run_prefix=run_indbc_0 --levels=1 --cpu_num=10 --eval_num=2 --final_test=0 --tensorflow_oppo=1 --max_steps=500000 --dataset_name=norm_medium

Notations:

1. We initially included the evaluation process in the training module in the form of a subprocess. However, we discovered that it resulted in reduced training efficiency. Thus, we decided to separate the evaluation process into a standalone module.
2. The <root_path> and <run_prefix> have the same meaning as in training module.
3. The <levels> represents the level of the opponents for evaluation and <tensorflow_oppo> represents the opponents load tensorflow pretrained models if =1 else pytorch.
4. <cpu_num> and <eval_num> have the same meaning as in sampling module and the total number of evaluation trajectories is cpu_num*eval_num.
5. <final_test>: A value of 1 means that only the last three models will be evaluated, and their average performance will be recorded into 'offline_logs/final_win_rate.xlsx' as final performance. A value of 0 indicates that the system will continuously monitor the model pool and evaluate the latest model whenever a new model is added, until <max_steps> is reached.

Acknowledgement

We gratefully acknowledge the support and contributions from Tencent TiMi Studio and Tencent AI Lab in our research. Portions of the code used in this work are adapted from the open-source code of hok_env. Specifically, we thank the developers for making their code available for reuse. Without their efforts, our research would not have been possible. We sincerely appreciate their generosity and the impact they have on our work.

Citation

If you use the code in this repository, please cite our paper as follows.

@inproceedings{qu2024hokoff,
  title={Hokoff: Real Game Dataset from Honor of Kings and its Offline Reinforcement Learning Benchmarks},
  author={Qu, Yun and Wang, Boyuan and Shao, Jianzhun and Jiang, Yuhang and Chen, Chen and Ye, Zhenbin and Liu, Linc and Yang, Junfeng and Lai, Lin and Qin, Hongyang and others},
  booktitle={Thirty-seventh Conference on Neural Information Processing Systems Track on Datasets and Benchmarks},
  year={2023}
}