[Docs] Readme update

.github/unittest/install_dependencies.sh

@@ -1,7 +1,7 @@
 
 
 python -m pip install --upgrade pip
-python -m pip install flake8 pytest pytest-cov hydra-core
+python -m pip install flake8 pytest pytest-cov hydra-core tqdm
 
 if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
 

README.md

@@ -1,9 +1,88 @@
 # BenchMARL
+[![tests](https://github.com/facebookresearch/BenchMARL/actions/workflows/unit_tests.yml/badge.svg)](test)
+[![Python](https://img.shields.io/badge/python-3.8%20%7C%203.9%20%7C%203.10-blue.svg)](https://www.python.org/downloads/)
 
+```bash
+python benchmarl/run.py algorithm=mappo task=vmas/balance
+```
+
+BenchMARL is a Multi-Agent Reinforcement Learning (MARL) training library created to enable reproducibility
+and benchmarking across different MARL algorithms and environments.
+Its mission is to present a standardized interface that allows easy integration of new algorithms and environments to 
+provide a fair comparison with existing solutions.
+BenchMARL uses [TorchRL](https://github.com/pytorch/rl) as its backend, which grants it high performance 
+and state-of-the-art implementations. 
+BenchMARL data reporting is compatible with [marl-eval](https://github.com/instadeepai/marl-eval) for standardized and
+statistically strong evaluations.
+
+- [BenchMARL](#benchmarl)
+  * [How to use](#how-to-use)
+    + [Notebooks](#notebooks)
+    + [Install](#install)
+    + [Run](#run)
+  * [Concept](#concept)
+    + [Experiment](#experiment)
+    + [Algorithms](#algorithms)
+    + [Environments](#environments)
+    + [Models](#models)
+  * [Reporting and plotting](#reporting-and-plotting)
+  * [Extending](#extending)
+  * [Configuring](#configuring)
+    + [Algorithm](#algorithm)
+    + [Task](#task)
+    + [Model](#model)
+      - [Sequence model](#sequence-model)
+  * [Features](#features)
+    + [Logging](#logging)
+    + [Checkpointing](#checkpointing)
+    + [Callbacks](#callbacks)
+
+
+## How to use
+
+### Notebooks
+
+### Install
+
+#### Install TorchRL
+
+Currently BenchMARL uses the latest version of TorchRL, 
+this will be installed automatically in future versions.
+
+```bash
+pip install git+https://github.com/pytorch-labs/tensordict
+git clone https://github.com/pytorch/rl.git
+cd rl
+python setup.py develop
+cd ..
+```
+
+#### Install BenchMARL
+You can just install it from github
+```bash
+pip install git+https://github.com/facebookresearch/BenchMARL
+```
+Or also clone it locally to access the configs and scripts
+```bash
+git clone https://github.com/facebookresearch/BenchMARL.git
+pip install -e BenchMARL
+```
+
+### Run
+
+
+## Concept
+### Experiment
+### Algorithms
+### Environments
+### Models
 
+## Reporting and plotting
 
+## Extending
 
-## Hydra config
+
+## Configuring
 
 Running custom experiments is extremely simplified by the [Hydra](https://hydra.cc/) configurations.
 
@@ -74,7 +153,10 @@ Configuring a layer
 python hydra_run.py "model.layers.l1.num_cells=[3]"
 ```
 
-## Logging
+
+## Features
+
+### Logging
 
 BenchMARL is compatible with the [TorchRL loggers](https://github.com/pytorch/rl/tree/main/torchrl/record/loggers).
 A list of logger names can be provided in the [experiment config](benchmarl/conf/experiment/base_experiment.yaml).
@@ -86,3 +168,6 @@ python hydra_run.py "experiment.loggers=[wandb]"
 
 Additionally, you can specify a `create_json` argument which instructs the trainer to output a `.json` file in the
 format specified by [marl-eval](https://github.com/instadeepai/marl-eval).
+
+### Checkpointing 
+### Callbacks

setup.py

@@ -8,9 +8,9 @@
     author="Matteo Bettini",
     author_email="[email protected]",
     packages=find_packages(),
-    install_requires=["torchrl", "tqdm", "hydra-core"],
+    install_requires=["tqdm", "hydra-core"],
     extras_require={
-        "tasks": ["vmas>=1.2.10", "pettingzoo[all]>=1.24.1"],
+        "vmas": ["vmas>=1.2.10"],
+        "pettingzoo": ["pettingzoo[all]>=1.24.1"],
     },
-    include_package_data=True,
 )