marl-lab
multi-agent reinforcement learning, training live in your browser — no server, no library, just two Q-tables
episode return (last 200 episodes)
0
episodes
0%
success (last 100)
0
collisions / ep
■ and ■ are independent Q-learners. Each must reach its dashed goal, but the only way through the wall is the single door — and colliding hurts (−8).
Watch the collisions/ep stat: early on they crash at the door constantly; within a few thousand episodes they learn to stagger their crossing — coordination emerging from nothing but reward.
Under the hood: independent tabular Q-learning (each agent observes both positions, 49×49 states × 5 actions), ε-greedy exploration, α = 0.2, γ = 0.95. The door is the classic bottleneck coordination problem — a toy version of the credit-assignment and coordination questions in my ongoing MARL research. Longer treatment: Albrecht, Christianos & Schäfer, MARL (2024).
