update readme, dockerfile

Dockerfile

@@ -17,7 +17,7 @@ RUN apt-get update && \
     apt-get install -y tmux
 
 #jaxmarl from source if needed, all the requirements
-RUN pip install -e .
+RUN pip install -e .[algs,dev]
 
 USER ${MYUSER}
 

README.md

@@ -36,6 +36,8 @@
 
 ## Multi-Agent Reinforcement Learning in JAX
 
+🎉 Update: JaxMARL was accepted at NeurIPS 2024 on Datasets and Benchmarks Track. See you in Vacouver!
+
 JaxMARL combines ease-of-use with GPU-enabled efficiency, and supports a wide range of commonly used MARL environments as well as popular baseline algorithms. Our aim is for one library that enables thorough evaluation of MARL methods across a wide range of tasks and against relevant baselines. We also introduce SMAX, a vectorised, simplified version of the popular StarCraft Multi-Agent Challenge, which removes the need to run the StarCraft II game engine. 
 
 For more details, take a look at our [blog post](https://blog.foersterlab.com/jaxmarl/) or our [Colab notebook](https://colab.research.google.com/github/FLAIROx/JaxMARL/blob/main/jaxmarl/tutorials/JaxMARL_Walkthrough.ipynb), which walks through the basic usage.
@@ -72,7 +74,7 @@ We follow CleanRL's philosophy of providing single file implementations which ca
 
 <h2 name="install" id="install">Installation 🧗 </h2>
 
-**Environments** - Before installing, ensure you have the correct [JAX version](https://github.com/google/jax#installation) for your hardware accelerator. The JaxMARL environments can be installed directly from PyPi:
+**Environments** - Before installing, ensure you have the correct [JAX installation](https://github.com/google/jax#installation) for your hardware accelerator. We have tested up to JAX version 0.4.25. The JaxMARL environments can be installed directly from PyPi:
 
 ```
 pip install jaxmarl 
@@ -84,12 +86,14 @@ pip install jaxmarl
     ```
     git clone https://github.com/FLAIROx/JaxMARL.git && cd JaxMARL
     ```
-2. The requirements for IPPO & MAPPO can be installed with:
+2. Requirements can be installed with:
     ``` 
-    pip install -e .
+    pip install -e .[algs]
     export PYTHONPATH=./JaxMARL:$PYTHONPATH
     ```
 
+**Development** - If you would like to run our test suite, install the additonal dependencies with `pip install -e .[dev]`.
+
 <h2 name="start" id="start">Quick Start 🚀 </h2>
 
 We take inspiration from the [PettingZoo](https://github.com/Farama-Foundation/PettingZoo) and [Gymnax](https://github.com/RobertTLange/gymnax) interfaces. You can try out training an agent in our [Colab notebook](https://colab.research.google.com/github/FLAIROx/JaxMARL/blob/main/jaxmarl/tutorials/JaxMARL_Walkthrough.ipynb). Further introduction scripts can be found [here](https://github.com/FLAIROx/JaxMARL/tree/main/jaxmarl/tutorials).
@@ -151,10 +155,12 @@ JAX-native algorithms:
 - [Mava](https://github.com/instadeepai/Mava): JAX implementations of IPPO and MAPPO, two popular MARL algorithms.
 - [PureJaxRL](https://github.com/luchris429/purejaxrl): JAX implementation of PPO, and demonstration of end-to-end JAX-based RL training.
 - [Minimax](https://github.com/facebookresearch/minimax/): JAX implementations of autocurricula baselines for RL.
+- [JaxIRL](https://github.com/FLAIROx/jaxirl?tab=readme-ov-file): JAX implementation of algorithms for inverse reinforcement learning.
 
 JAX-native environments:
 - [Gymnax](https://github.com/RobertTLange/gymnax): Implementations of classic RL tasks including classic control, bsuite and MinAtar.
 - [Jumanji](https://github.com/instadeepai/jumanji): A diverse set of environments ranging from simple games to NP-hard combinatorial problems.
 - [Pgx](https://github.com/sotetsuk/pgx): JAX implementations of classic board games, such as Chess, Go and Shogi.
 - [Brax](https://github.com/google/brax): A fully differentiable physics engine written in JAX, features continuous control tasks.
 - [XLand-MiniGrid](https://github.com/corl-team/xland-minigrid): Meta-RL gridworld environments inspired by XLand and MiniGrid.
+- [Craftax](https://github.com/MichaelTMatthews/Craftax): (Crafter + NetHack) in JAX.

pyproject.toml

@@ -44,7 +44,7 @@ dependencies = [
 ]
 
 [project.optional-dependencies]
-alg = [
+algs = [
   "optax",
   "distrax",
   "safetensors",