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Codes accompanying the paper "Learning Nearly Decomposable Value Functions with Communication Minimization" (ICLR 2020)

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# NDQ: Learning Nearly Decomposable Value Functions with Communication Minimization

## Note
 This codebase accompanies paper [Learning Nearly Decomposable Value Functions with Communication Minimization](https://openreview.net/forum?id=HJx-3grYDB&noteId=HJx-3grYDB), 
 and is based on  [PyMARL](https://github.com/oxwhirl/pymarl) and [SMAC](https://github.com/oxwhirl/smac) codebases which are open-sourced.

The implementation of the following methods can also be found in this codebase, which are finished by the authors of [PyMARL](https://github.com/oxwhirl/pymarl):

- [**QMIX**: QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://arxiv.org/abs/1803.11485)
- [**COMA**: Counterfactual Multi-Agent Policy Gradients](https://arxiv.org/abs/1705.08926)
- [**VDN**: Value-Decomposition Networks For Cooperative Multi-Agent Learning](https://arxiv.org/abs/1706.05296) 
- [**IQL**: Independent Q-Learning](https://arxiv.org/abs/1511.08779)

Build the Dockerfile using 
```shell
cd docker
bash build.sh
```

Set up StarCraft II and SMAC:
```shell
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 `hallway`.

```shell
python3 src/main.py 
--config=categorical_qmix
--env-config=join1
with
env_args.n_agents=2
env_args.state_numbers=[6,6]
obs_last_action=False
comm_embed_dim=3
c_beta=0.1
comm_beta=1e-2
comm_entropy_beta=0.
batch_size_run=16
t_max=2e7
local_results_path=$DATA_PATH
is_cur_mu=True
is_rank_cut_mu=True
runner="parallel_x"
test_interval=100000
```

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 NDQ on SC2 tasks, run the following command:
```shell
--config=categorical_qmix
--env-config=sc2
with
env_args.map_name=bane_vs_hM
env_args.sight_range=2
env_args.shoot_range=2
env_args.obs_all_health=False
env_args.obs_enemy_health=False
comm_embed_dim=3
c_beta=0.1
comm_beta=0.0001
comm_entropy_beta=0.0
batch_size_run=16
runner="parallel_x"
```

SMAC maps can be found in src/smac_plus/sc2_maps/.

All results will be stored in the `Results` folder.

## 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:

```shell
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.

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