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README.md

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-# Recurrent Multi-Agent Deep Deterministic Policy Gradient
+## Recurrent Multi-Agent Deep Deterministic Policy Gradient (Rec-MADDPG)
+
+This is the code for implementing the Rec-MADDPG algorithm presented in my MSc Dissertation "Communication and Cooperation in Decentralized Multi-AgentReinforcement Learning". It is configured to be run in conjunction with environments from the Multi-Agent Particle Environments (MPE).
+
+### Installation
+- Install requirements with `pip install -r requirements.txt`
+- Install my adaption of the Multi-Agent Particle environment: https://github.com/nicoring/multiagent-particle-envs
+
+### Usage
+- `cd` into the `maddpg` directory
+- Run the code with `python trainer.py --scenario SCENARIO_NAME`
+- `python trainer.py --help` gives a description of all the available command line options.
+- The code stores the success rates and returns as well as the policies of the agents.
+
+### Code Structure
+This repository contains the code for MADDPG and Rec-MADDPG in the maddpg directory, which contains the following files:
+ - `trainer.py` which is the main file to run and contains the training logic.
+ - `agent.py` contains the code for MADDPG and Rec-MADDPG agents.
+ - `models.py` contains the code for the actor and policy networks.
+ - `memory.py` contains the replay buffer code.
+ - `distribitions.py` contains the code for the KL-divergence between Gumbel-Softmax distributions
+ - Additionally, there are multiple run scripts.