[ot][ai] Open Source Human Influence
Undescribed Horrific Abuse, One Victim & Survivor of Many
gmkarl at gmail.com
Tue Nov 29 04:46:50 PST 2022
>From last week, facebook/meta open sourced their "Cicero" AI that is
architectured around playing an online human interaction game called
It's an analog to google's "alpha go" but for this online world empire
game. The primary behavior is chatting with other players to convince
them what to do.
marketing website: https://ai.facebook.com/research/cicero/
Transcription of sample dialog found on web:
FRA [human]: "A little nervous with all your fleets there."
FRA [human]: "Going to put MAO in SPA-SC"
ITA [cicero]: "I get it, I really do. Don't worry, I'm going to move
Tunis to Ionian. I have no reason to attack you right now, I'm going
ITA [cicero]: "I can totally understand your nervousness."
FRA [human]: "Thanks--I'll leave SPA-nc at once after."
Code for Cicero, an AI agent that plays the game of Diplomacy with
open-domain natural language negotiation.
MIT License + AGPL components
README.md commit cf8f134806976a6d9663d340122ab9a690a7c27a
# Diplomacy Cicero and Diplodocus
This code contains checkpoints and training code the following papers:
* ["Human-Level Play in the Game of Diplomacy by Combining Language
Models with Strategic
published in Science, November 2022.
* ["Mastering the Game of No-Press Diplomacy via Human-Regularized
Reinforcement Learning and
Planning"](https://arxiv.org/abs/2210.05492) in review at ICLR 2023.
A very brief orientation:
- Most of the language modeling and generation code is in
[parlai_diplomacy](parlai_diplomacy), and leverages the [ParlAI
framework](https://github.com/facebookresearch/ParlAI) for running and
finetuning the language models involved.
- Within the [agents](fairdiplomacy/agents) directory, the central
logic for Cicero's strategic planning lives
[here](fairdiplomacy/agents/bqre1p_agent.py). The latter also contains
the core logic for Diplodocus's strategic planning. "bqre1p" was the
internal dev name for DiL-piKL, and "br_corr_bilateral" the internal
dev name for Cicero's bilateral and correlated planning components.
- The dialogue-free model architectures for RL are
and the bulk of the training logic lives
- The RL training code for both Cicero and Diplodocus is
- The [conf](conf) directory contains various configs for Cicero,
Diplodocus, benchmark agents, and training configs for RL.
- A separately licensed subfolder of this repo
[here](fairdiplomacy_external) contains some utilities for visually
rendering games, or connecting agents to be run online.
### Game info
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,
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
To get the game's spirit, watch
You can play the game online on
[webDiplomacy](https://webdiplomacy.net/) either against bots or
# Clone the repo with submodules:
git clone --recursive
git at github.com:facebookresearch/diplomacy_cicero.git diplomacy_cicero
# Apt installs
apt-get install -y wget bzip2 ca-certificates curl git build-essential
clang-format-8 git wget cmake build-essential autoconf libtool
# Install conda
wget --quiet https://repo.anaconda.com/miniconda/Miniconda3-4.7.10-Linux-x86_64.sh
/bin/bash ~/miniconda.sh -b
# Create conda env
conda create --yes -n diplomacy_cicero python=3.7
conda activate diplomacy_cicero
# Install pytorch, pybind11
conda install --yes pytorch=1.7.1 torchvision cudatoolkit=11.0 -c pytorch
conda install --yes 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 --yes go protobuf=3.19.1
# Install python requirements
pip install -r requirements.txt
# Local pip installs
pip install -e ./thirdparty/github/fairinternal/postman/nest/
# NOTE: Postman here links against pytorch for tensors, for this to work you may
# need to separately have installed cuda 11 on your own.
pip install -e ./thirdparty/github/fairinternal/postman/postman/
pip install -e . -vv
# Run unit tests
After each pull it's recommended to run `make` to re-compile internal
C++ and protobuf code.
### Downloading model files
Please email <diplomacyteam at meta.com> to request the password. Then
run `bash bin/download_model_files.sh <PASSWORD>`. This will download
and decrypt all relevant model files into `./models`. This might take
### Accessing Cicero's experiment games
JSON data and visualizations for games that Cicero played in are
located in [data/cicero_redacted_games](data/cicero_redacted_games).
Only conversations with players who have consented to having their
dialogue released are included. Please refer to the
subdirectory for details on HTML visualizations.
### Getting started
The front-end for most tasks is `run.py`, which can run various tasks
specified by a protobuf config. The config schema can be found at
`conf/conf.proto`, and example configs for different tasks can be
found in the `conf` folder. This can be used for most tasks (except
training parlai models): training no-press models, comparing agents,
profiling things, launching an agent on webdip, etc.
The config specification framework, called HeyHi, [is explained
A core abstraction is an `Agent`, which is specified by an `Agent`
config whose schema lives in `conf/agents.proto`.
### Simulating games between agents
To simulate 1v6 games between a pair of agents, you can run the
`compare_agents` task. For example, to play one Cicero agent as Turkey
against six full-press imitation agents, you can run
`python run.py --adhoc --cfg conf/c01_ag_cmp/cmp.prototxt
If you don't have sufficient memory to load two agents, you can load a
single agent in self-play with the `use_shard_agent=1` flag:
`python run.py --adhoc --cfg conf/c01_ag_cmp/cmp.prototxt
### Training models in RL
To run the training for Cicero and/or Diplodocus:
python run.py —adhoc —cfg
python run.py —adhoc —cfg
The above training commands are designed for running on an
appropriately configured Slurm cluster with a fast cross-machine
shared filesystem. One can also instead pass
`launcher.local.use_local=true` to run them on locally, e.g. on an
individual 8-GPU-or-more GPU machine but training may be very slow.
### Other tasks
See [here](fairdiplomacy_external) for some separately-licensed code
for rendering game jsons with HTML, as well as connecting agents to
run on [webdiplomacy.net](https://webdiplomacy.net).
### Supervised training of baseline models
Supervised training and/or behavioral cloning for various
dialogue-conditional models as well as pre-RL baseline dialogue-free
models involves some of the scripts in
[parlai_diplomacy](parlai_diplomacy) via the ParlAI framework, and on
the dialogue-free side, some of the configs
However the dataset of human games and/or dialogue is NOT available
here, so the relevant code and configs are likely to be of limited
use. They are provided here mostly as documentation for posterity.
However, as mentioned above pre-trained models are available, and with
sufficient compute power, re-running the RL on top of these
pre-trained models is also possible without any exteral game data.
### Pre-commit hooks
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](https://github.com/psf/black)
auto-formatter to format all python code.
For protobufs use `clang-format-8 conf/*.proto -i`.
To run tests locally run `make test`.
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 aims to use the same entry point as users do, i.e.,
`run.py` for the HeyHi part and `diplom` for the ParlAi.
We use `pytest` to run and discover tests. Some useful
To run all tests in your current directory, simply run:
To run tests from a specific file, run:
To use name-based filtering to run tests, use the flag `-k`. For
example, to only run tests with `parlai` in the name, run:
pytest -k parlai
For verbose testing logs, use `-v`:
pytest -v -k parlai
To print the output from a test or set of tests, use `-s`; this also
allows you to set breakpoints:
To view the durations of all tests, run with the flag `--durations=0`, e.g.:
pytest --durations=0 unit_tests/
The following license, which is also available [here](LICENSE.md),
covers the content in this repo *except* for the
[fairdiplomacy_external](fairdiplomacy_external) directory. The
content of fairdiplomacy_external is separately licenced under a
version of the AGPL, see the license file within that directory for
(covers this repo except for the fairdiplomacy_external directory)
Copyright (c) Meta, Inc. and its affiliates.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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