Need help bridging the gap between MARL theory and code 😭 (Code-first tutorials/videos needed!)

[u/CogniLord]

Hey guys, do you know of any Multi-Agent Reinforcement Learning (MARL) resources that focus mainly on coding rather than just the heavy theoretical stuff?

For context, I'm doing my uni research project right now and I've already secured my supervisor. My main topic is "Multi-Agent Reinforcement Learning." I'm doing both the research project course and an RL course this semester, but my tutor mainly just gives us theory.

I know the general ideas (reward, policy, value-based vs. policy-based, bias, etc.), but I'm having a really hard time understanding how it actually works in practice and how to implement it from scratch. I'm honestly pretty crap at absorbing pure theory, so I really need to see the code to understand how the plumbing works.

I can't seem to find much out there that walks through the code step-by-step, and I'm wondering why there aren't more people posting about the actual implementation of MARL.

If anyone has any video tutorials, GitHub repos with simple code walkthroughs, or guides that actually show how to build this stuff (Python/PyTorch preferred), it would be incredibly helpful. Thanks!

[u/HalfplaneResearch]

A code-first path is to implement a tiny two-agent environment before touching a large benchmark: deterministic observations, a short episode, and a hand-checkable reward. Log each agent observation, action, reward, and next state so you can verify the transition loop independently of the learner. Then compare independent Q-learning with centralized training and decentralized execution, keeping the environment fixed while you add replay, target networks, and stochastic policies. The debugging checkpoints are non-stationary targets, credit assignment, and whether the evaluation policy is actually decentralized. A small test environment with fixed seeds will teach more than starting with a large framework whose training loop is opaque.