Reward Conditioned Policy Learning From Task Outcomes

via “reward function design and shaping for complex multi-objective 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: Combines potential-based reward shaping with multi-objective weighting to balance lap time, safety, and race position, using domain knowledge about racing physics to structure rewards that guide learning without over-constraining agent behavior or creating conflicting gradient signals