From 368c6173577d0f9c0ad70fb5b4b6afa12c864c15 Mon Sep 17 00:00:00 2001
From: Siyi Hu <48464760+Theohhhu@users.noreply.github.com>
Date: Mon, 13 Nov 2023 12:08:39 +1100
Subject: [PATCH] Update README.md

---
 README.md | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 10f576ba..92c01a58 100644
--- a/README.md
+++ b/README.md
@@ -31,8 +31,10 @@
 | **March 2023** :anchor:We are excited to announce that a major update has just been released. For detailed version information, please refer to the [version info](https://github.com/Replicable-MARL/MARLlib/releases/tag/1.0.2).|
 | **May 2023** Exciting news! MARLlib now supports five more tasks: [MATE](https://marllib.readthedocs.io/en/latest/handbook/env.html#mate), [GoBigger](https://marllib.readthedocs.io/en/latest/handbook/env.html#gobigger), [Overcooked-AI](https://marllib.readthedocs.io/en/latest/handbook/env.html#overcooked-ai), [MAPDN](https://marllib.readthedocs.io/en/latest/handbook/env.html#power-distribution-networks), and [AirCombat](https://marllib.readthedocs.io/en/latest/handbook/env.html#air-combat). Give them a try!|
 | **June 2023** [OpenAI: Hide and Seek](https://marllib.readthedocs.io/en/latest/handbook/env.html#hide-and-seek) and [SISL](https://marllib.readthedocs.io/en/latest/handbook/env.html#sisl) environments are incorporated into MARLlib.|
-| **Aug 2023** :tada:MARLlib has been accepted for publication in [JMLR](https://www.jmlr.org/).|
+| **Aug 2023** :tada:MARLlib has been accepted for publication in [JMLR](https://www.jmlr.org/mloss/).|
 | **Sept 2023** Latest [PettingZoo](https://marllib.readthedocs.io/en/latest/handbook/env.html#pettingzoo) with [Gymnasium](https://gymnasium.farama.org) are compatiable within MARLlib.|
+| **Nov 2023** We are currently in the process of creating a hands-on MARL book and aim to release the draft by the end of 2023.|
+
 
 **Multi-agent Reinforcement Learning Library ([MARLlib](https://arxiv.org/abs/2210.13708))** is ***a MARL library*** that utilizes [**Ray**](https://github.com/ray-project/ray) and one of its toolkits [**RLlib**](https://github.com/ray-project/ray/tree/master/rllib). It offers a comprehensive platform for developing, training, and testing MARL algorithms across various tasks and environments.