Less is more -- the Dispatcher/ Executor principle for multi-task Reinforcement Learning

Researchers propose a dispatcher/executor principle for multi-task Reinforcement Learning that partitions controllers into task-understanding and device-specific components connected by a regularized communication channel. This structural approach aims to improve generalization and data efficiency as an alternative to simply scaling large neural networks with vast datasets.

• →The dispatcher/executor principle splits RL controllers into task comprehension and execution components to boost generalization.
• →This approach emphasizes structural design principles over pure scaling with large neural networks and massive datasets.
• →The method aims to improve data efficiency when training data is limited rather than abundant.
• →The communication channel between dispatcher and executor uses strong regularization to enforce abstraction.
• →The research positions itself as complementary to scaling trends while addressing data scarcity scenarios.