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# Learning to Cooperate with Humans using Generative Agents

## Description

Training framework of **G**enerative **A**gent **M**odeling for **M**ulti-agent **A**daptation (**GAMMA**)

paper: [Learning to Cooperate with Humans using Generative Agents (NeurIPS 2024)](https://openreview.net/forum?id=v4dXL3LsGX)

demo/website: [https://sites.google.com/view/human-ai-gamma-2024/](https://sites.google.com/view/human-ai-gamma-2024/)

model checkpoints: [https://github.com/lych1233/Overcooked_Population_Data/tree/main]()

## Get Started

## Install

First you need to have a python environment with pytorch. If you don't have, here is an example of creating a vitual conda environment:

```bash
conda create -n marl python=3.12
conda activate marl
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
```

And then install other packages. These (and a few missing) packages suffice to work on top of an minimal pytorch virtual environment (conda/docker)

```bash
pip install absl-py dill numpy scipy tqdm gym pettingzoo ipython pygame ipywidgets opencv-python
pip install wandb icecream setproctitle seaborn tensorboardX slackweb psutil slackweb pyastar2d einops h5py
```

To install module "mapbt" for MARL and "overcooked" for Overcooked!

```bash
git clone git@github.com:lych1233/mapbt.git
# Move to the root directory
cd mapbt
# Install mapbt
pip install -e .
# Install overcooked
pip install -e mapbt/envs/overcooked/overcooked_berkeley
```

## Run (Example: Overcooked)

For a complete pipeline to train a Cooperator for overcooked, please refer to [this document](mapbt/scripts/overcooked_population/README.md)

Here we only show some simple examples.

### Self-play on Overcooked

```bash
# cd mapbt/scripts
bash train_overcooked_sp.sh 
```

### Zero-shot Coordination on Overcooked

#### Generate Config

(Optional) Use the following script to generate config files if the config files are absent:

```bash
# cd mapbt/scripts
bash generate_overcooked_policy_config.sh
```

The config files can also be found under the "run_dir" of current experiment

#### Stage 1: Population-based Training

```bash
# cd mapbt/scripts
bash train_overcooked_mep_stage1.sh
```

### Stage 2: Train the Adaptive Policy

Copy the model to this folder: "mapbt/scripts/policy_models". Specifiy the path to the policy models that will be used in stage 2 in the ``--population_yaml_path'' file

Then run

```bash
# cd mapbt/scripts
bash train_overcooked_mep_stage2.sh
```

### Evaluatiuon with Human Proxy Model

```bash
# Train the human proxy model by behavior cloning
bash train_overcooked_bc.sh
# Move the model to mapbt/scripts/policy_models
cp "mep_adaptive_model" "mapbt/scripts/policy_models/[layout_name]/mep_adaptive.pt"
cp "bc_model" "mapbt/scripts/policy_models/[layout_name]/proxy.pt"
# Evaluate
bash eval_overcooked_mep_vs_proxy.sh
```

## Cite

## Reference

This repository is built upon [the official implementation](https://github.com/samjia2000/HSP) of [this paper](https://openreview.net/forum?id=TrwE8l9aJzs):

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