This code contains checkpoints and training code the following papers:
- "Human-Level Performance in No-Press Diplomacy via Equilibrium Search" from ICLR'21. Exact code version is at
iclr21branch. - "No-press Diplomacy From Scratch" from NeurIPS'21.
A very brief orientation:
- The implementation for SearchBot lives here
- The supervised learning model architecture lives here, and the bulk of the training logic lives here
- RL training - both for policy gradient and deep nash value iteration - lives here
-
The pretrained models are available for download under this repo's "Releases". The easiest way to download them is via
bin/download_dora_models.shscript. Configurations of agents, i.e., combinations of checkpoints and parameters are stored in conf/common/agents. Below is the list of the most important ones:model_sampledis Blueprint from ICLR'21 papersearchbot_02_fastbotis SearchBot from ICLR'21 papersearchbot_neurips21_fva_dorais the FvA DORA agent that played with humans from NeurIPS21 paper.searchbot_neurips21_human_dnvi_npuis HumanDNVI-NPU from NeurIPS21 papersearchbot_neurips21_dorais DORA from NeurIPS21 papersearchbot_neurips21_supervisedis SearchBot-Transf from NeurIPS21 paper.
The command to play a couple of agents against each other:
python run.py --adhoc --cfg conf/c01_ag_cmp/cmp.prototxt \
I.agent_one=agents/CFG_NAME1.prototxt \
I.agent_six=agents/CFG_NAME2.prototxt
Runn an FVA game:
python run.py --adhoc --cfg conf/c01_ag_cmp/cmp.prototxt \
I.agent_one=agents/CFG_NAME1.prototxt \
I.agent_six=agents/CFG_NAME2.prototxt \
start_game=./fva_starting_position.json
There is also a script that will run sequentially multiple games between all pairings of a set of agents.
python conf/exps/h2h_example.py
It will run a few 1vs6 games of DipNet vs Human-DNVI-NPU and print averate score:
--> square_score
agent_six dipnet
agent_one
human_dnvi_npu 0.287
Note that it may take a long time to run many games - if you have multiple GPUs you may prefer your own infrastructure to parallelize things.
For more info about the configuration and command-line args, see the Running Tasks section below.
See iclr21 branch.
Diplomacy is a strategic board game set in 1914 Europe. The board is divided into fifty-six land regions and nineteen sea regions. Forty-two of the land regions are divided among the seven Great Powers of the game: Austria-Hungary, England, France, Germany, Italy, Russia, and Turkey. The remaining fourteen land regions are neutral at the start of the game.
Each power controls some regions and some units. The number of the units controlled depends on the number of the controlled key regions called Supply Centers (SCs). Simply put, more SCs means more units. The goal of the game is to control more than half of all SCs by moving units into these regions and convincing other players to support you.
You can find the full rules here. To get the game's spirit, watch some games with comments. What's more, you can play the game online on WebDiplomacy either against bots or humans.
# Clone the repo with submodules:
git clone --recursive [email protected]:facebookresearch/diplomacy_searchbot.git
cd diplomacy_searchbot
# Apt installs
apt-get install -y wget bzip2 ca-certificates curl git build-essential clang-format-8 git wget cmake build-essential autoconf libtool pkg-config libgoogle-glog-dev
# Install conda
wget --quiet https://repo.anaconda.com/miniconda/Miniconda3-4.7.10-Linux-x86_64.sh -O ~/miniconda.sh
/bin/bash ~/miniconda.sh -b
# Create conda env
conda create --yes -n diplomacy python=3.7
source activate diplomacy
# Install pytorch, pybind11
conda install --yes pytorch=1.7.1 torchvision cudatoolkit=11.0 -c pytorch
conda install pybind11
# Install go for boringssl in grpc
# We have some hacky patching code for protobuf that is not guaranteed
# to work on versions other than this.
conda install go protobuf=3.19.1 --yes
# Install python requirements
pip install -r requirements.txt
# Local pip installs
pip install -e ./thirdparty/github/fairinternal/postman/nest/
pip install -e ./thirdparty/github/fairinternal/postman/postman/
pip install -e . -vv
# Make
make
# Run unit tests
make test_fast
After each pull it's recommended to run make to re-compile internal C++ and protobuf code.
The code has a single entry point, run.py, that can be used to train a model, compare agents, profile them, etc.
We refer to this kind of activity as a task.
To specify which task to run and what parameters to use, we use configs.
Below an example of a config that is used to train an agent with imitation learning on human data:
train {
dataset_params: {
data_dir: "/path/to/games/"
value_decay_alpha: 0.9;
}
batch_size: 2500;
lr: 0.001;
lr_decay: 0.99;
clip_grad_norm: 0.5
checkpoint: "./checkpoint.pth";
lstm_dropout: 0.1;
encoder_dropout: 0.2;
num_encoder_blocks: 8;
}
We use text protobuf format to specify the configs. Each task has a schema, a formal description of what parameters are allowed in each config, e.g., here's the definition for the train task above.
Protobufs could be confusing, but good news - you don't have to understand them to run tasks. Instead, you need to find the config for your task and run it. We describe all tasks in the next section. Here is an example of how to launch training on human data:
python run.py --adhoc --cfg conf/c02_sup_train/sl_20200901.prototxt
You can override any config parameter in command line using argparse-like syntax:
python run.py --adhoc --cfg conf/c02_sup_train/sl_20200901.prototxt batch_size=200 --dataset_params.value_decay_alpha=1.0
Note that it's optional to use "--" in front of overrides.
Check documentation for HeyHi, the configuration library, for more details.
In general, all configs are stored in conf/ folder and grouped by tasks. You can find all possible arguments for all tasks in conf/conf.proto file. Below are the most important tasks:
- Supervised training task. Configs in
c02_sup_train, docs here. - Making 2 agents play against each other for evaluation. Configs in
c01_ag_cmp, docs here. - Training RL agent. Configs in
c04_exploit, docs here.
We use an in-house fast C++ implementation of the diplomacy environment. See here for how to interact with it.
The games could be serialized as JSON files, e.g., our human data and test situations use this format.
Code structure:
- fairdiplomacy - datasets, agents, and trainers
- conf - all the configs for fairdiplomacy/ part.
External links:
- "No Press Diplomacy: Modeling Multi-Agent Gameplay" (Paquette et al, 2019), on which our supervised learning is based.
- "Learning to Play No-Press Diplomacy with Best Response Policy Iteration" (Anthony et al, 2020). We use some model improvement from the paper.
Run pre-commit install to install pre-commit hooks that will auto-format python code before commiting it.
Or you can do this manually. Use black auto-formatter to format all python code.
For protobufs use clang-format-8 conf/*.proto -i.
Circle CI tests check for that.
To run tests locally run make test. Or you can wait Circle CI to run the test once you push your changes to any branch.
We have 2 level of tests: fast, unit tests (run with make test_fast) and slow, integration tests (run with make test_integration).
The latter aim to use the same entry point as users do, i.e., run.py
There are some differences between running the tests locally vs on CI.
- Most integration tests use small fake data in the repo, but some use real data to check that the latest models are working. Obviuously, these tests are skipped on CI and so local tests have better coverage.
- CI use CPUs for everything.
We use nose to discover tests.
It searches for *test*.py files and extracts all unittests from them. So usually your tests will be automatically included into CircleCI.
Some useful commands. Integration tests are notoriously slow and so sometimes one want to execute only one particular test. Here's how to do this. First, list all the test:
$ nosetests integration_tests/integration_test.py --collect-only -v --with-id
#2 integration_test.test_build_cache ... ok
#3 integration_test.test_rl_configs('exploit_06.prototxt', {}) ... ok
#3 integration_test.test_rl_configs('selfplay_01.prototxt', {}) ... ok
#4 integration_test.test_rl_configs_real_data('exploit_06.prototxt', {}) ... ok
#4 integration_test.test_rl_configs_real_data('selfplay_01.prototxt', {}) ... ok
#5 integration_test.test_train_configs('sl.prototxt', {}) ... ok
...
And then pass the id of the test to the nose:
nosetests integration_tests/integration_test.py -v --with-id 3
The diplomacy model was trained via supervised learning on a dataset of 46,148 games provided by webdiplomacy.net.
The dataset can be downloaded from this repo's "Releases" page. The file has been compressed (via gzip) and encrypted (via gpg) with a password. To obtain the password, please email [email protected]
Once you have downloaded the data_cache.pt.gz.gpg file, you can run:
gpg data_cache.pt.gz.gpg
and enter the password to decrypt the file, and then:
gunzip data_cache.pt.gz
to uncompress the file. Then you can train a model by running the command in the overview section of this README
Note that re-training the blueprint model is not necessary to run an agent comparison -- a pre-trained model can be downloaded from this repo's "Releases" page.
MIT License
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