Automated curricula through setter-solver interactions

Understand

Reinforcement learning algorithms use correlations between policies and rewards to improve agent performance.

• But in dynamic or sparsely rewarding environments these correlations are often too small, or rewarding events are too infrequent to make learning feasible.
• Human education instead relies on curricula--the breakdown of tasks into simpler, static challenges with dense rewards--to build up to complex behaviors.
• While curricula are also useful for artificial agents, hand-crafting them is time consuming.