Reward Shaping And Curriculum Learning For Complex Locomotion Tasks

via “multi-agent reinforcement learning with curriculum learning for complex control tasks”

* ⭐ 02/2022: [Magnetic control of tokamak plasmas through deep reinforcement learning](https://www.nature.com/articles/s41586-021-04301-9%E2%80%A6)

Unique: Uses a carefully designed curriculum learning pipeline with progressive difficulty stages (single-agent time trials → multi-agent racing → championship scenarios) combined with distributed PPO training across GPU clusters, enabling agents to learn racing strategies that exceed human champion performance without explicit reward shaping for racing-specific behaviors