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Overview

Code for "Joint Policy Search for Collaborative Multi-agent Imperfect Information Games". Arxiv link. The paper is published in NeurIPS 2020.

The project aims to find better equilibrium in multi-agent collaborative games with imperfect information by improving the policies of multiple agents simultaneously. This helps escape local equilibrium where unlaterial improvement of one player's policy is not helpful. To achieve that, we developed a novel value decomposition technique that decomposes the expected value changes into information sets where the policy differs, and search over candidate information sets via depth-first search. Each update can be proven not to degrade the performance in the tabular cases.

We open source the code to reproduce our results in simple games (see Def. 1-3 in the paper). The dataset and pre-trained Bridge model will be released later.

@inproceedings{tian2020jps,
    title={Joint Policy Search for Multi-agent Collaboration with Imperfect Information},
    author={Yuandong Tian and Qucheng Gong and Tina Jiang},
    booktitle={NeurIPS},
    year={2020}
}

Requirements

Compiled with Linux and GCC 7.4 Pytorch 1.5+ (libtorch)

Compilation

First initialize all submodules:

git submodule update --init --recursive

Then go to simple_game, and do the following to build

mkdir build
cd build
cmake .. 
make

The executable jps is in ./build.

Examples

To start, in the build directory, run the following to get CFR1k+JPS solution for Mini-Hanabi. Log here:

./jps --game comm2 --iter 100 --iter_cfr 1000

You might run with --num_samples 1 to get the results for sampled-based version. E.g., run the following to get results of 100 trials:


for i in `seq 1 100`; do ./jps --game comm2 --iter 100 --iter_cfr 1000 --seed $i --num_samples 1; done > aa.txt
grep "CFRPure" aa.txt

Another example: Simple Bidding (N=16, d=3). Log here.

./jps --game simplebidding --seed 1 --iter 100 --N_minibridge 16 --iter_cfr 1000 --max_depth 3

There are a few tabular imperfect information collaborative games implemented:

  • comm: Simple Communication Game (Def. 1 in the paper)
  • simplebidding: Simple Bidding (Def. 2 in the paper)
  • 2suitedbridge: 2-Suit Mini-Bridge (Def. 3 in the paper)
  • comm2: Mini-Hanabi introduced in BAD paper.

Result Simple Game Result

Results of sample-based approach Sampled-based Result

Visualize policy (for simple bidding)

In build folder, do:

./jps --game=simplebidding --N_minibridge=4 --seed=2 > aa.txt
python ../load_strategies.py --log aa.txt

The output is:

Optimal policies
score: 2.1875
        0       1        2        3
0  10 (0)  10 (1)  120 (2)  120 (2)
1  20 (0)  20 (2)   20 (2)  230 (4)
2  20 (2)  20 (2)   20 (2)  230 (4)
3  30 (0)  30 (4)   30 (4)   30 (4)

Note that "120" means P1 first bids 1, P2 then bids 2 and P1 bids 0 (Pass). The final contract is 2^{2-1} = 2, if card1 + card2 >= 2, then both of the players get reward 2 (shown in the parentheses), otherwise 0.

Bridge Dataset

Please download the dataset here. The training set (dda.db) contains 2.5M situations, each with a pre-computed double dummy table. The database file can be opened by sqlite3.

$ sqlite3
SQLite version 3.31.1 2020-01-27 19:55:54
Enter ".help" for usage hints.
Connected to a transient in-memory database.
Use ".open FILENAME" to reopen on a persistent database.
sqlite> .open dda.db
sqlite> select * from records limit 1;
0|{"pbn": "[Deal \"N:KT9743.AQT43.J.7 J85.9.Q6.KQJ9532 Q2.KJ765.T98.T64 A6.82.AK75432.A8\"]", "ddt": [0, 12, 0, 12, 0, 12, 0, 12, 10, 3, 10, 3, 9, 4, 9, 4, 0, 8, 0, 8]}

DDS table is in the format of C (NESW), D (NESW), H (NESW), S (NESW), NT (NESW). For example, the 1D ddt table above means that

C D H S NT
N 0 0 10 9 0
E 12 12 3 4 8
S 0 0 10 9 0
W 12 12 3 4 8

Contribution

See the CONTRIBUTING file for how to help out.

License

JPS is under CC-BY-NC 4.0 license, as found in the LICENSE file.