Prerequisites
You do not need previous experience with multi-agent reinforcement learning.
You also do not need prior experience with LLM agents or multi-agent LLM systems. The tutorial first develops the cooperative MARL ideas needed to understand that frontier.
A basic foundation in the following areas is enough.
Mathematics
Section titled “Mathematics”You should be comfortable with:
- basic algebra;
- sums and averages;
- basic probability;
- reading simple mathematical notation.
Review resources:
Advanced calculus and mathematical derivations are not required.
Python
Section titled “Python”You should be able to read:
- variables and functions;
- lists and dictionaries;
- loops and conditionals;
- simple NumPy operations.
Review resources:
The labs provide most of the implementation. You will mainly complete small pieces of code, run experiments, and interpret results.
Reinforcement Learning
Section titled “Reinforcement Learning”You should understand:
- agents and environments;
- observations and actions;
- rewards and returns;
- policies.
Review resource:
The Background section develops the RL and MARL foundations needed before Chapter 1: Coordinate.
Google Colab
Section titled “Google Colab”The Communicate, Adapt, and Challenge labs use Google Colab.
You should know how to:
- run a notebook cell;
- change a value and rerun a cell;
- inspect plots and outputs.
Review resource:
The notebooks use the
cooperative-marl-labs package and are designed
to run on free Colab CPU sessions.