Issue #110 RL Edwin Dong
Multi-Agent RL, Simply Explained
Free editionPRO edition
This week’s PRO issue includes a Colab notebook with practice exercises and solutions for multi-agent setups. We’ll dive into techniques and the overall CTDE loop, also seeing how they work with interactive graphs.
Please do read the full article before attempting the code tutorial.
As always, the code in this issue can be executed on a normal home PC and does not require a large amount of VRAM.
The monthly interview question bank can be found here. It has been updated for July!
These are industry standard questions that are tested across major tech companies, AI research firms, and more. The questions are sorted by difficulty and topic.
Check back frequently for more questions, and let us know what type of questions you would like to see next!
A single reinforcement learning (RL) agent learns in a closed feedback loop. It observes an environment, picks an action, and updates its policy based on the reward it receives.
The environment may be complex, but from the agent's point of view, it behaves consistently enough to learn useful patterns from.
Multi-agent reinforcement learning (MARL) is a field of RL that deals with environments that are not as stable, where multiple agents share and interact within the same environment.
In MARL, agents act in parallel in a single environment, trained on rewards that depend on what the other agents do.
Picture a self-driving car navigating an intersection, a robot-controlled warehouse, or traffic signals coordinating roads. These are all examples of MARL problems.
Most of MARL research (old and new) is about routing around a dynamic, complex environment without giving up what makes RL useful in the first place.
This edition is for PRO members.
