The Sparse Reward Problem
We were training a multi-agent Reinforcement Learning system to coordinate logistics in a warehouse simulation. The goal was for agents to find a package, pick it up, and navigate a maze to deliver it.
When we started training them on the full task, the reward graph remained flat at zero. The agents never stumbled upon the correct sequence of behaviors by random exploration, so they never received a positive reward signal.
Applying Curriculum Learning
We solved this by applying Curriculum Learning—breaking the complex task down into progressively harder stages.
- Stage 1: We spawned the package right next to the delivery zone. Agents were rewarded just for touching it.
- Stage 2: We moved the package further away.
- Stage 3: We added maze walls.
- Stage 4: We added multiple agents that needed to avoid colliding.
By slowly increasing the complexity of the environment and tightening the reward function only after the agent mastered the previous stage, the agents successfully learned the optimal collaborative policy.