multi agent reinforcement learning in congestion games
A free browser game — play it in your browser, no install. Exact atomic Braess analysis and deterministic multi-agent learning, presented as an interactive Three.js essay
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Live interactive essay · Experiment methodology · Study guide
Each episode represents a new morning commute. Independent tabular learners separately choose complete routes from source S to destination T, experience only their own trip, and update only their own selected action. They do not meet, message, or exchange Q-values. Their shared congestion couples their rewards without creating communication between learners. With the central link closed, an even split gives every commuter a 90-minute trip.
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