AI · 1h ago
Ablation reveals objective beacon is make-or-break for multi-agent RL
In a cooperative multi-agent RL setup with 8 agents on a 100x100 grid, removing an objective beacon caused success rate to drop from 99.5% to 0%. The beacon provides directional bearing to goals outside the agents' 11x11 egocentric window. Without it, agents performed no better than random policy, highlighting the critical role of observation design in partially observable tasks.
Meridian48 take
The result is a stark reminder that in RL, what the agent sees often matters more than the algorithm itself — a lesson easily overlooked in the rush to scale models.
Read the full reporting
One channel decided whether my multi-agent RL agents learned at all →
DEV Community
multi-agent-rlreinforcement-learning