wjh720 / QPLEX

QPLEX: Duplex Dueling Multi-Agent Q-Learning

Note

This codebase accompanies paper Duplex Dueling Multi-Agent Q-Learning, and is based on PyMARL and SMAC codebases which are open-sourced. The modified SMAC of QPLEX is illustrated in the folder QPLEX_smac_env of supplymentary material.

The implementation of the following methods can also be found in this codebase, which are finished by the authors of following papers:

• QPLEX: QPLEX: Duplex Dueling Multi-Agent Q-Learning
• QTRAN: QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement learning
• QMIX: QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
• COMA: Counterfactual Multi-Agent Policy Gradients
• VDN: Value-Decomposition Networks For Cooperative Multi-Agent Learning
• IQL: Independent Q-Learning

Build the Dockerfile using

cd docker
bash build.sh

Set up StarCraft II and SMAC:

bash install_sc2.sh

This will download SC2 into the 3rdparty folder and copy the maps necessary to run over.

The requirements.txt file can be used to install the necessary packages into a virtual environment (not recomended).

Run an experiment

The following command train NDQ on the didactic task matrix_game_2 .

python3 src/main.py 
--config=qplex 
--env-config=matrix_game_2 
with 
local_results_path='../../../tmp_DD/sc2_bane_vs_bane/results/' 
save_model=True use_tensorboard=True 
save_model_interval=200000 
t_max=210000 
epsilon_finish=1.0

The config files act as defaults for an algorithm or environment.

They are all located in src/config. --config refers to the config files in src/config/algs --env-config refers to the config files in src/config/envs

To train QPLEX on SC2 offline setting tasks, run the following command:

Construct the dataset:

python3 src/main.py 
--config=qmix 
--env-config=sc2 
with 
env_args.map_name=1c3s5z 
env_args.seed=1 
local_results_path='../../../tmp_DD/sc2_1c3s5z/results/' 
save_model=True 
use_tensorboard=True 
save_model_interval=200000 
t_max=2100000 
is_save_buffer=True 
save_buffer_size=20000 
save_buffer_id=0

Training with offline data collection:

python3 src/main.py 
--config=qplex_sc2 
--env-config=sc2 
with 
env_args.map_name=1c3s5z 
env_args.seed=1 
local_results_path='../../../tmp_DD/sc2_1c3s5z/results/' 
save_model=True 
use_tensorboard=True 
save_model_interval=200000 
t_max=2100000 
is_batch_rl=True 
load_buffer_id=0

To train QPLEX on SC2 online setting tasks, run the following command:

python3 src/main.py 
--config=qplex_qatten_sc2 
--env-config=sc2 
with 
env_args.map_name=3s5z 
env_args.seed=1 
local_results_path='../../../tmp_DD/sc2_3s5z/results/' 
save_model=True 
use_tensorboard=True 
save_model_interval=200000 
t_max=2100000 
num_circle=2

SMAC maps can be found in in the folder QPLEX_smac_env of supplymentary material.

Saving and loading learnt models

Saving models

You can save the learnt models to disk by setting save_model = True, which is set to False by default. The frequency of saving models can be adjusted using save_model_interval configuration. Models will be saved in the result directory, under the folder called models. The directory corresponding each run will contain models saved throughout the experiment, each within a folder corresponding to the number of timesteps passed since starting the learning process.

Loading models

Learnt models can be loaded using the checkpoint_path parameter, after which the learning will proceed from the corresponding timestep.

Watching StarCraft II replays

save_replay option allows saving replays of models which are loaded using checkpoint_path. Once the model is successfully loaded, test_nepisode number of episodes are run on the test mode and a .SC2Replay file is saved in the Replay directory of StarCraft II. Please make sure to use the episode runner if you wish to save a replay, i.e., runner=episode. The name of the saved replay file starts with the given env_args.save_replay_prefix (map_name if empty), followed by the current timestamp.

The saved replays can be watched by double-clicking on them or using the following command:

python -m pysc2.bin.play --norender --rgb_minimap_size 0 --replay NAME.SC2Replay

Note: Replays cannot be watched using the Linux version of StarCraft II. Please use either the Mac or Windows version of the StarCraft II client.